Browsing by Author "Rony, Md Awlad Hossen"

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A Dual-stage Polyp Segmentation Network with a Custom Attention-based U-net and Segment Anything Model for Enhanced Mask Prediction
(Elsevier, 2024-11-14) Islam, Radiful; Akash, Rashik Shahriar; Rony, Md Awlad Hossen; Hasan, Md Zahid
Early detection of colorectal cancer through the proper segmentation of polyps in the colonoscopy images is crucial. Polyps' complex morphology and varied appearances are the greatest obstacles for the segmentation approaches. The paper introduces SAMU-Net, a novel deep learning-based dual-stage architecture consisting of a custom attention-based U-Net and modified Segment Anything Model (SAM) for better polyp segmentation. In our model, we used the custom U-Net architecture with an attention mechanism to obtain polyp segmentation masks as the first stage. This mask is then used to generate a bounding box input for the second stage that contains the modified Segment Anything Model. The modified SAM relies on the use of High-Quality token-based architecture along with global and local properties to segment polyps accurately, even in cases where the shapes and sizes of polyps are diverse and the polyps have different appearances. The efficiency of SAMU-Net generated from four different datasets of colonoscopy images was examined. Our process produced a dice coefficient score of 0.94, which is very impressive and has a considerable improvement over the existing state-of-the-art polyp segmentation methods. Moreover, the qualitative results also visualize that the SAMU-Net is capable of accurately segmenting polyps of wide ranges, thus, it is a relevant tool for computer-aided detection as well as the diagnosis of colorectal cancer.
Development of an Automated Optimal Distance Feature-Based Decision System for Diagnosing Knee Osteoarthritis Using Segmented X-Ray Images
(Elsevier, 2023-11-03) Fatema, Kaniz; Rony, Md Awlad Hossen; Azam, Sami; Mukta, Md Saddam Hossain; Karim, Asif; Hasan, Md Zahid; Jonkman, Mirjam
Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.
SAMU-Net: A dual-stage polyp segmentation network with a custom attention-based U-Net and segment anything model for enhanced mask prediction
(Scopus, 2024-12-24) Islam, Radiful; Akash, Rashik Shahriar; Rony, Md Awlad Hossen; Hasan, Md Zahid
Early detection of colorectal cancer through the proper segmentation of polyps in the colonoscopy images is crucial. Polyps' complex morphology and varied appearances are the greatest obstacles for the segmentation approaches. The paper introduces SAMU-Net, a novel deep learning-based dual-stage architecture consisting of a custom attention-based U-Net and modified Segment Anything Model (SAM) for better polyp segmentation. In our model, we used the custom U-Net architecture with an attention mechanism to obtain polyp segmentation masks as the first stage. This mask is then used to generate a bounding box input for the second stage that contains the modified Segment Anything Model. The modified SAM relies on the use of High-Quality token-based architecture along with global and local properties to segment polyps accurately, even in cases where the shapes and sizes of polyps are diverse and the polyps have different appearances. The efficiency of SAMU-Net generated from four different datasets of colonoscopy images was examined. Our process produced a dice coefficient score of 0.94, which is very impressive and has a considerable improvement over the existing state-of-the-art polyp segmentation methods. Moreover, the qualitative results also visualize that the SAMU-Net is capable of accurately segmenting polyps of wide ranges, thus, it is a relevant tool for computer-aided detection as well as the diagnosis of colorectal cancer.