Dissertations/Theses - Department of Biomedical Engineering
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Item Impact of hematological and morphological variations at circle of willis on cerebrovascular hemodynamics(Department of Biomedical Engineering (BME), BUET, 2025-05-19) Jafrin Sultana; Tarik Arafat, Dr. MuhammadCerebrovascular diseases, including ischemic stroke and intracranial hemorrhage, remain leading global health burdens. The Circle of Willis (CoW), a critical arterial network for cerebral perfusion, is highly influenced by hematological factors, such as blood viscosity and hematocrit levels, as well as anatomical variations like hypoplasia, agenesis, and asymmetry. This study employs Computational Fluid Dynamics (CFD) simulations on patient-specific CoWs’ geometries to investigate the combined impact of hematocrit variation, stenosis morphology, and anatomical abnormalities on intracranial hemodynamics. Using ANSYS software and modeling blood as a non-Newtonian Carreau fluid under pulsatile flow, key hemodynamic indices as wall shear stress (WSS), time average wall shear stress, oscillatory shear index, velocity, translesional pressure ratio, pressure drop index, and stroke risk index, were computed to characterize cerebrovascular behavior under diverse physiological and pathological states. The influence of varying hematocrit levels on wall shear stress and cerebral perfusion is observed using computational fluid dynamics models in normal and aneurysmal geometries. Elevated hematocrit increased blood viscosity and WSS, while lower hematocrit led to reduced shear forces. The WSS-viscosity relationship was nonlinear: low WSS regions were linked to endothelial apoptosis and aneurysm formation, whereas high WSS areas correlated with increased rupture risk. Stenosis geometry and CoW integrity as key determinants of hemodynamic cooperation were identified through nine design of experiments framework. Irregular stenosis and anatomical incompleteness impaired collateral flow, particularly during occlusion, elevating ischemic susceptibility. Hemodynamic metrics indicated that local stress variations promoted atherogenesis and increased thromboembolic risk. Results emphasize the correlation among CoW morphology, hematological factors, and flow dynamics in cerebrovascular pathology. CFD modeling, integrated with clinical data, offers a robust platform for individualized risk assessment and therapeutic planning. Future research incorporating real-time imaging and AI-driven analysis may enhance predictive accuracy and improve stroke prevention strategies. Keywords: Stroke; Ischemic Stroke; Hematological and Morphological Variation; Circle of Willis; Cerebrovascular Hemodynamics; Computational Fluid DynamicsItem Diagnosis-oriented learned image compression system for chest radiographs with Regions-of-interest control(Department of Biomedical Engineering, BUET, 2025-05-28) Ibn Sabur Khan Nuhash, Shoyad.; Hasan Al Banna, Dr. TaufiqThis thesis presents DiagLIC, a diagnosis-oriented learned image compression (LIC) framework specifically designed for chest radiographs. Unlike conventional codecs and general-purpose LIC models that prioritize visual fidelity or bitrate efficiency alone, the proposed method in- corporates both diagnostic awareness and region-of-interest (ROI) preservation directly into the compression objective. Leveraging a transformer-based architecture with prompt-conditioned Swin Transformer blocks, the system supports variable-rate compression through a single model and allocates higher representational capacity to clinically significant regions such as lesions, opacities, or cardiomegaly zones. The framework is jointly optimized using a composite loss function that integrates rate-distortion and classification performance, ensuring that compressed images retain features essential for downstream diagnosis. Empirical evaluation on NIH and VinDr-CXR subsets demonstrates that DiagLIC significantly outperforms classical codecs (e.g., JPEG, WebP, JPEG2000) and state-of-the-art learned compression baselines in both PSNR and diagnostic accuracy. Across six quality levels, DiagLIC consistently achieves higher PSNR (e.g., 40.42 dB at 0.0951 bpp) and yields an average weighted PSNR of up to 52.49 dB, with ROI-specific PSNRs exceeding NROI PSNRs by over 1 dB at all levels. For diagnostic classifi- cation, DiagLIC maintains high fidelity, with only a minimal drop in average AUC (0.014) com- pared to uncompressed images, achieving AUC scores above 0.70 across all 8 tested patholo- gies and no degradation in certain categories such as Nodule/Mass. Statistical analysis confirms that the improvements in rate-distortion performance over baselines are significant, while the reduction in diagnostic performance remains clinically negligible. Qualitative comparisons fur- ther reveal that DiagLIC effectively suppresses artifacts and preserves lesion-level details, even at aggressive compression. This work demonstrates the feasibility and clinical value of inte- grating semantic objectives into image compression models, laying the groundwork for future diagnosis-preserving compression techniques in medical imaging.Item Leveraging frequency domain attributes for multi-modal medical image segmentation using convolutional neural networks(Department of Biomedical Engineering, BUET, 2025-05-28) Shams Nafisa Ali; Hasan Al Banna, Dr. TaufiqRecent advances in deep learning have significantly enhanced medical image segmen- tation. As medical data becomes increasingly diverse and complex, the need for ar- chitectures that can generalize across modalities and anatomical structures has grown paramount. While CNNs, Transformers, and their hybrid architectures have addressed issues such as limited receptive fields and redundant feature representations, most mod- els remain confined to the spatial domain—overlooking the frequency domain’s rich structural cues. Some recent studies have explored spectral information at the feature level; however, frequency-domain integration at the supervision level remains largely untapped. To this end, we propose Phi-SegNet, a CNN-based architecture that incor- porates phase-aware cues at both architectural and optimization levels. The network integrates Bi-Feature Mask Former (BFMF) modules that blend neighboring encoder features to reduce semantic gaps, and Reverse Fourier Attention (RFA) blocks that re- fine decoder outputs using phase-regularized embeddings. A dedicated phase-aware loss aligns these embeddings with structural priors, forming a closed feedback loop that emphasizes boundary precision. Evaluated on five public datasets spanning ultra- sound, X-ray, histopathology, MRI, and colonoscopy, Phi-SegNet consistently achieves state-of-the-art performance, particularly excelling in fine-grained boundary segmen- tation tasks. On average, across these five datasets, Phi-SegNet achieves a relative improvement of 1.54% ± 1.26% in IoU and 1.10% ± 0.69% in F1-score over the next best-performing model for each dataset. Additionally, under generalized training using a unified dataset comprising all five modalities, as well as in cross-dataset generaliza- tion scenarios involving unseen datasets from the known domain, Phi-SegNet exhibits robust and superior performance—highlighting its adaptability and modality-agnostic design. These findings demonstrate the potential of leveraging spectral priors in both learning and supervision, offering a new direction toward generalized, universal, and anatomically precise segmentation frameworks.Item Automated denoising and classification of lung sounds using end-to-end deep learning models(Department of Biomedical Engineering, BUET, 2025-04-19) Samiul Based Shuvo; Hasan Al Banna, Dr. TaufiqLung sound auscultation is essential for monitoring respiratory health, especially in regions facing a shortage of skilled healthcare workers. Automated analysis of respi- ratory sounds has the potential to provide significant clinical support in such settings. However, lung sounds (LS) are frequently contaminated by various noise sources such as heart sounds, background conversations, and movement artifacts which makes accu- rate interpretation challenging. Conventional denoising techniques often fail to address these challenges due to the spectral overlap between noise and respiratory signals in real-world clinical recordings. In addition to that, while respiratory sound classification has been widely studied in adults, its application in pediatric populations, particularly in children aged <=6 years, remains a complex and underexplored area. The developmen- tal changes in pediatric lungs considerably alter the acoustic properties of respiratory sounds, necessitating specialized classification approaches tailored to this age group. To address these challenges and advance automated lung sound analysis, this thesis is divided into two major components. In the first part, a specialized deep-denoiser model (Uformer) has been proposed for lung sound denoising. The proposed Uformer model consists of three modules: a Convolutional Neural Network (CNN) encoder module dedicated to extracting latent features, a Transformer encoder module employed to en- hance the encoding of unique LS features further and effectively capture intricate long- range dependencies, and a CNN decoder module employed to generate the denoised signals. The performance of the proposed Uformer model has been evaluated on lung sounds induced with different types of synthetic and real-world noise. The proposed model showed an average output SNR of 16.51 dB when evaluated with -12 dB LS signals. Our end-to-end model, with an average output SNR of 19.31 dB, outperforms the existing model, achieving nearly double the performance, when evaluated with am- bient noise and fewer parameters. Based on the qualitative and quantitative findings in this study, it can be stated that the proposed denoising model is robust and gener- alized to assist with monitoring respiratory conditions. The second part of the thesis focuses on the classification of pediatric respiratory sounds. A multistage hybrid CNN- Transformer architecture has been proposed for detecting respiratory diseases from both entire recordings and individual breath cycles using scalogram images of the signals. To fill the gap in pediatric classification, the SPRSound dataset, comprising recordings from children with an average age of 5.5 years, has been utilized for two-level classifica- tion tasks. The proposed classification model utilizes CNN-extracted respiratory sound features from scalogram images, integrating an attention framework to improve predic- tive performance. The proposed framework achieved a score of 0.9039 in binary event classification and 0.8448 in multiclass event classification. At the record level, ternary classification yielded a score of 0.720, while multiclass record classification attained 0.571. However, our proposed method consistently demonstrates a 3.81% and 5.94% performance gain over the existing best model, respectively, in these record-level clas- sification tasks. These proposed approaches can significantly aid in the diagnosis and prediction of the severity of respiratory diseases in both developing and underdeveloped nations.Item Development of gum-based chitosan nanoparticle with improved functional properties for conjunctivitis treatment(Department of Biomedical Engineering, BUET, 2025-02-05) Afiya Mubasharah; Tarik Arafat, Dr. MuhammadConjunctivitis, commonly known as pink eye, is a widespread eye condition characterized by inflammation or infection of the conjunctiva, the thin membrane covering the white part of the eye and the inner eyelids. Treating conjunctivitis effectively can be challenging due to the limitations of traditional drug delivery methods, such as eye drops, which often do not provide sufficient drug retention and can lead to systemic side effects. This study presents an innovative dual-drug delivery approach that involves encapsulating two powerful medications—dexamethasone (DEX), a corticosteroid that reduces inflammation, and moxifloxacin (MOX), an antibiotic effective against bacterial infections—within chitosan nanoparticles (CSNPs). These nanoparticles are further enhanced with various natural gums used as crosslinker, including Arabic gum, tamarind gum, guar gum, gellan gum, and xanthan gum. The inclusion of these gums aims to improve the mucoadhesive properties of the formulations, which helps them adhere better to the ocular surface and increases the availability of the drugs where they are needed most. The study conducted comprehensive evaluations through various tests—in vitro, ex vivo, and in vivo to assess the effectiveness, biocompatibility, and ability of these formulations to retain drugs on the eye's surface. Among the formulations tested, Arabic gum-based chitosan nanoparticles (CS-A-0.03) showed optimal characteristics, including a particle size of ~200 nm and high stability. These nanoparticles demonstrated excellent drug encapsulation efficiency (97 ± 1.96%. for DEX, 58.72 ± 5% for MOX) and controlled release over 5 hours. The formulation exhibited biocompatibility with only 0.28±0.09% hemolysis and no corneal tissue damage. Notably, the CS-A nanoparticles showed 73.91±0.25% mucoadhesion, significantly higher than commercial eye drops (64±3.5%). In vivo tests revealed faster and more effective inflammation reduction compared to conventional treatments. The long-term stability study showed that CS-A based formulations remained stable for 12 months at room temperature. The results demonstrated significant improvements in therapeutic outcomes when compared to conventional eye drop treatments. Specifically, these new formulations showed enhanced drug retention time on the ocular surface, addressing critical issues associated with traditional treatments.Item Fabrication of Tannic Acid Crosslinked Gelatin-CMC Based Electrospun Matrix for Combatting Antibacterial Resistance in Infected Wounds(Department of Biomedical Engineering, BUET, 2024-09-14) Jinia, Rafia Hasnat; Arafat, Dr. Muhammad TarikThe global rise in antibiotic-resistant infections has become a critical challenge in wound management, with conventional treatments often proving ineffective. Electrospun nanofibers offer a promising solution by providing a versatile platform that combines antimicrobial efficacy with enhanced wound healing capabilities. Herein, this study pioneers an advanced electrospun nanofiber matrix, designed to revolutionize wound care by simultaneously addressing antibiotic resistance and promoting superior wound healing. The naofibrous matrix integrates gelatin and CMC, with TA serving as a crosslinker, significantly reinforcing both its structural integrity(up to 1.70±0.46 MPa) and biocompatibility (< 2%). The innovative synergy of these biopolymers endows the matrix with extraordinary antimicrobial potency, exhibiting unmatched efficacy against both pathogenic Gram-positive and Gram-negative bacterial strains—surpassing the capabilities of current commercial alternatives compromised by emerging resistance. This extraordinary performance stems from unmatched physicochemical properties of the developed matrix, featuring exceptional water uptake and a beneficial amorphous state, which together create an ideal environment for rapid, superior wound healing. In vitro assays, alongside excisional wound studies in mice, demonstrated that wounds treated with the fabricated matrix exhibited significantly accelerated and more effective healing compared to the control groups, achieving up to 95% wound closure across all bacterial species tested. To unravel the sophisticated interactions between matrix components and bacterial cells, in silico simulations were employed, providing a deep mechanistic understanding of its superior performance. The findings underscore the potential of this advanced electrospun matrix as a multifaceted platform for next-generation wound care, providing a robust solution to the escalating issue of antibiotic-resistant infections while fostering an optimal healing environment.Item Development of antimicrobial peptide loaded PVA-based nanofibrous matrix to combat bacterial wound infection(Department of Biomedical Engineering, BUET, 2024-08-31) Firdous, Syeda Omara; Arafat, Dr. Muhammad TarikWound infections caused by antibiotic resistant bacterial strains, present a significant challenge in healthcare worldwide due to the uncontrolled use of antibiotics. A promising alternative to conventional antibiotics is antimicrobial peptides (AMPs) that provide potent, broad spectrum antibacterial activity with a reduced likelihood of resistance development due to their action mechanism. This research focuses on developing and evaluating AMP loaded PVA nanofibrous matrix as an innovative approach to treating these infections. AMPs extracted from corn seeds, were stabilized within PVA nanofibers through in situ incorporation facilitated by dopamine, resulting in robust and stable nanofibrous matrices. Physical and chemical characterizations, including FTIR, XRD, and tensile test, indicated that the 2:1-PVA/AMP formulation offered superior structural integrity with a tensile strength of 3.2 ± 0.3 MPa, making it the an effective option for further testing. This formulation showed a significant zone of inhibition (ZOI) of 19 ± 0.3 mm against Staphylococcus aureus, 16 ± 0.3 mm against Escherichia coli, 48 ±0.1 mm against Proteus spp., and 16 ± 0.1 mm against Pseudomonas aeruginosa, outperforming the 1:1-PVA/AMP. Comparatively, ceftazidime (CAZ) showed no antibacterial activity across all tested strains. Hemocompatibility assessments revealed that all PVA/AMP samples exhibited a hemolysis ratio below 3%, confirming their safety for blood contact. In vivo experiments using an infected wound model in mice demonstrated that the 2:1-PVA/AMP nanofibers significantly accelerated wound healing, with markedly reduced pus formation and superior tissue regeneration compared to both the control and PVA/CAZ groups. Histological analysis showed a marked reduction in the epithelial gap and enhanced re-epithelialization, with 87 ± 2% wound closure observed by day 14, compared to 44 ± 1% in the PVA/CAZ group. Additionally, a significant reduction in inflammatory cell infiltration was observed in the 2:1-PVA/AMP group which indicates better management of infection and inflammation. This contributed to more effective tissue regeneration compared to both the control and PVA/CAZ groups. These results underscore the potential of AMP loaded PVA nanofibers as a superior alternative to conventional antibiotic treatments in wound care, particularly for managing infections caused by antibiotic resistant bacteria.Item Natural polysaccharide modified olive oil based nanoemulsion as a controlled delivery vehicle for topical formulations(Department of Biomedical Engineering (BME), BUET, 2024-06-23) Kobra., Khadijatul; Arafat, Dr. Muhammad TarikNanoemulsions facilitate efficient drug delivery through the skin, overcoming the limitations of traditional topical formulations. However, their poor stability, low viscosity, and rapid drug release restrict their application in topical delivery. Nanoemulsions with enhanced stability, modified rheology, and increased viscosity, such as shear-thinning behavior, are well-accepted by patients due to their dosage integrity, controlled drug release properties, and ease of application. For oil-in-water emulsions, these challenges can be addressed by optimizing nanoemulsion formulation and modifying the rheological behavior of the continuous phase with biopolymers without significantly impacting the emulsion properties. Therefore, in this study, olive oil nanoemulsion was formulated and modified with xanthan gum and gum acacia to explore as a potential controlled topical delivery vehicle. Oil-in-water nanoemulsion formulated with the optimized composition of olive oil, tween 80, and water was used as the drug carrier and further modified with natural polysaccharides. The effect of gum on nanoemulsion different physiochemical characteristics, stability, rheology, drug release, and encapsulation efficiency were investigated. Results showed that the developed nanoemulsion behaved as low viscosity Newtonian fluid and released 100% drug within 6 h. Modification with xanthan and gum acacia had significantly improved formulation viscosity, drug encapsulation efficiency (> 85%), and controlled drug release by up to 40% with a release pattern following Korsmeyer–Peppas model. Additionally, xanthan gum modified formulation exhibited shear thinning rheology by forming an extended network in the continuous phase, whereas gum acacia modified formulation behaved as Newtonian fluid at high shear rate (> 200 s-1). Furthermore, xanthan gum modified formulations had improved zeta potential, stability, monodispersity, and hemocompatibility and showed high antibacterial activity against S. aureus than gum acacia modified formulations. These results indicate the higher potential of xanthan gum modified formulation as a topical delivery vehicle. In addition, the skin irritation test confirmed the safety of the developed formulations for topical use. Finally, an in vivo skin penetration test was conducted to assess the distribution and penetration of ciprofloxacin in various layers of the skin. The study found that the highest concentration of the drug was present in the stratum corneum, with drug concentration decreasing in the deeper layers of the skin.Item Investigation of chitosan/starch- based polyphenolic acid crosslinked antibacterial microparticles for rapid hemostasis(Department of Biomedical Engineering (BME), BUET, 2024-07-01) Tithy, Lamiya Hassan; Arafat, Dr. Muhammad TarikHemorrhage is a major contributor to avoidable fatalities worldwide. It occurs when there is excessive blood loss due to accidents, battles, or surgical procedures. The preparation of rapid hemostatic materials along with strong bactericidal property and minimum erythrocyte lysis for deep hemorrhage still remains a challenge. Herein, a series of novel microparticle hemostats made with different polysaccharides such as chitosan, starch, cross linked with unoxidized tannic acid was prepared via hydrothermal treatment and facile ionic gelation method. Hemostats' comparative functional properties, such as adjustable antibacterial and erythrocyte compatibility upon various starch additions were evaluated. The chemical bonds between the constituent materials were verified by ATR-FTIR. The microparticles had excellent hemostatic property and could concentrate blood cells, blood proteins and clotting factors as well as could induce electrostatic interaction between positively charged chitosan and negatively charged blood cells. Blood clotting index (BCI), red blood cell (RBC) adhesion was performed to verify its hemostatic capability for in vitro study. Prothrombin time (PT) and activated partial thromboplastin time (aPTT) were employed to find out the composite’s hemostatic mechanism. For in vivo hemostatic study, mice liver laceration and rat tail amputation were performed to evaluate the composite’s efficacy in real injury scenario. The in vivo hemostatic study reveals that the developed hemostats for mouse liver laceration and rat tail amputation had clotting times (13 s and 38 s, respectively) and blood loss (51 mg and 62 mg, respectively) similar to those of CeloxTM. These studies suggest that the prepared microparticles can be employed in deep wound to halt bleeding. The erythrocyte adhesion test demonstrated that erythrocyte lysis can be lowered by modifying the antibacterial hemostats with different starches. Antibacterial efficacy of the hemostats remained intact against S. aureus (>90%), E. coli (>80%), and P. mirabilis bacteria upon starch modification. In comparison with CeloxTM, which does not have any antibacterial property against gram negative bacteria, the developed hemostats microparticles have broad-spectrum antibacterial activity. They also demonstrated high hemocompatibility (<3% hemolysis ratio), moderate cell viability (>81%), in vivo biodegradation, and angiogenesis indicating adequate biocompatibility and wound healing. These results suggest that the developed series of microparticle hemostats have a great promise as rapid hemostatic agent with strong antibacterial property to be used in emergency situations for preventing massive hemorrhage.Item Towards organ separation from projection radiographs using a semi-supervised deep learning-based technique(Department of Biomedical Engineering (BME), BUET, 2024-03-25) Kawsar Ahmed, Md.; Banna, Dr. Taufiq Hasan AlThe objective of this thesis is to develop a novel deep-learning method to separate organ-specific tissue images from projection radiographs, facilitating improved disease diagnosis by providing focused diagnostic information alongside conventional radiographs. This study proposes OrGAN, a generative adversarial network based model to translate chest X-rays into lung tissue images. It consists of a U-Net generator modified with a domain classifier and gradient reversal layer for domain adaptation, and a CNN discriminator. OrGAN was trained on 779 paired synthetic X-ray/lung images generated from CT data, alongside 15,000 unpaired real X-ray images from the VinDr-CXR dataset. Qualitative evaluation involved radiologist assessment of visual quality, anatomical accuracy and diagnostic utility on 10 test cases. Quantitative assessment compared lung disease classification performance using deep learning models trained on generated lung tissues, segmented lungs, and original X-rays across two independent datasets - VinDr-CXR (6 labels) and COVIDx-CXR-4 (COVID vs normal). OrGAN achieved high performance in generating lung tissue images on synthetic X-ray data (PSNR 28.8dB, SSIM 0.944 on test set). Qualitative evaluation by 5 radiologists indicated that the generated images enhanced visibility of lung features, preserved diagnostic information, and complemented X-rays for improved diagnosis. In quantitative tests, the DenseNet model trained on lung tissues outperformed those trained on segmented lungs, and actual X-ray, showing statistically significant (p-value < 0.05) improvements in overall F1-score, and sensitivity for a multilabel (6 labels) disease classification task on the VinDr dataset. On COVIDx, both of the two classifier models (DenseNet and ResNet) achieved higher overall F1-scores for a multiclass (2 classes) disease classification task, compared to using original X-rays or segmented lungs. The proposed OrGAN model effectively separates interpretable lung tissue images from X-ray projections. The generated images retain diagnostic fidelity, offer additional diagnostic insights to complement conventional X-rays, and can improve performance of deep learning models for precise disease diagnosis. Thus, the study proposes a potential pathway for further exploration into organ-specific tissue image separation from projection radiographs.