Browsing by Author "Ali, Md Mamun"

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Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier
(Tech Science Press, 2023-10-08) Mahmud, S M Hasan; Ali, Md Mamun; Shahriar, Mohammad Fahim; Al-Zahrani, Fahad Ahmed; Ahmed, Kawsar; Nandi, Dip; Bui, Francis M.
Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build a predictive model, open-source data was collected where five stages of images of AD were available as Cognitive Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and AD. Every stage of AD is considered as a class, and then the dataset was divided into three parts binary class, three class, and five class. In this research, we applied different preprocessing steps with augmentation techniques to efficiently identify AD. It integrates a random oversampling technique to handle the imbalance problem from target classes, mitigating the model overfitting and biases. Then three machine learning classifiers, such as random forest (RF), K-Nearest neighbor (KNN), and support vector machine (SVM), and two deep learning methods, such as convolutional neuronal network (CNN) and artificial neural network (ANN) were applied on these datasets. After analyzing the performance of the used models and the datasets, it is found that CNN with binary class outperformed 88.20% accuracy. The result of the study indicates that the model is highly potential to detect AD in the initial phase.
Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5
(Tech Science Press, 2023-01-01) Ontor, Md Zahid Hasan; Ali, Md Mamun; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed; Mahmud, S. M. Hasan; Azam, Sami
"Cervical Cancer (CC) is a rapidly growing disease among women throughout the world, especially in developed and developing countries. For this many women have died. Fortunately, it is curable if it can be diagnosed and detected at an early stage and taken proper treatment. But the high cost, awareness, highly equipped diagnosis environment, and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage. To solve this issue, the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images. The system is designed using the YOLOv5 (You Only Look Once Version 5) model, which is a deep learning method. To build the model, cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed. Then the YOLOv5 models were applied to the labeled dataset to train the model. Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage. All of the model’s variations performed admirably. The model can effectively detect cervical cancerous cell, according to the findings of the experiments. In the medical field, our study will be quite useful. It can be a good option for radiologists and help them make the best selections possible."
Heart Disease Prediction Using Supervised Machine Learning Algorithms
(Computers in Biology and Medicine, 2021) Ali, Md Mamun; Paul, Bikash Kumar; Ahmed, Kawsar; M.Bui, Francis; Quinn, Julian M.W.; Moni, Mohammad Ali
Machine learning and data mining-based approaches to prediction and detection of heart disease would be of great clinical utility, but are highly challenging to develop. In most countries there is a lack of cardiovascular expertise and a significant rate of incorrectly diagnosed cases which could be addressed by developing accurate and efficient early-stage heart disease prediction by analytical support of clinical decision-making with digital patient records. This study aimed to identify machine learning classifiers with the highest accuracy for such diagnostic purposes. Several supervised machine-learning algorithms were applied and compared for performance and accuracy in heart disease prediction. Feature importance scores for each feature were estimated for all applied algorithms except MLP and KNN. All the features were ranked based on the importance score to find those giving high heart disease predictions. This study found that using a heart disease dataset collected from Kaggle three-classification based on k-nearest neighbor (KNN), decision tree (DT) and random forests (RF) algorithms the RF method achieved 100% accuracy along with 100% sensitivity and specificity. Thus, we found that a relatively simple supervised machine learning algorithm can be used to make heart disease predictions with very high accuracy and excellent potential utility.
Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques
(Hindawi Publications, 2023-05-02) Biswas, Niloy; Ali, Md Mamun; Rahaman, Md Abdur; Islam, Minhajul; Mia, Md. Rajib; Azam, Sami; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed; Moni, Mohammad Ali
Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi-square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K-nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best-fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time.
Machine Learning-based Statistical Analysis for Early Stage Detection of Cervical Cancer
(Computers in Biology and Medicine, Elsevier, 2021-12) Ali, Md Mamun; Ahmed, Kawsar; Bui, Francis M.; Paul, Bikash Kumar; Ibrahim, Sobhy M.; Quinn, Julian M.W.; Moni, Mohammad Ali
Cervical cancer (CC) is the most common type of cancer in women and remains a significant cause of mortality, particularly in less developed countries, although it can be effectively treated if detected at an early stage. This study aimed to find efficient machine-learning-based classifying models to detect early stage CC using clinical data. We obtained a Kaggle data repository CC dataset which contained four classes of attributes including biopsy, cytology, Hinselmann, and Schiller. This dataset was split into four categories based on these class attributes. Three feature transformation methods, including log, sine function, and Z-score were applied to these datasets. Several supervised machine learning algorithms were assessed for their performance in classification. A Random Tree (RT) algorithm provided the best classification accuracy for the biopsy (98.33%) and cytology (98.65%) data, whereas Random Forest (RF) and Instance-Based K-nearest neighbor (IBk) provided the best performance for Hinselmann (99.16%), and Schiller (98.58%) respectively. Among the feature transformation methods, logarithmic gave the best performance for biopsy datasets whereas sine function was superior for cytology. Both logarithmic and sine functions performed the best for the Hinselmann dataset, while Z-score was best for the Schiller dataset. Various Feature Selection Techniques (FST) methods were applied to the transformed datasets to identify and prioritize important risk factors. The outcomes of this study indicate that appropriate system design and tuning, machine learning methods and classification are able to detect CC accurately and efficiently in its early stages using clinical data.
StackDPP: Stacking-Based Explainable Classifier for Depression Prediction and Finding the Risk Factors Among Clinicians
(MDPI Publications, 2023-08-20) Al-Zahrani, Fahad Ahmed; Abdulrazak, Lway Faisal; Ali, Md Mamun; Islam, Md Nazrul; Ahmed, Kawsar
Mental health is a major concern for all classes of people, but especially physicians in the present world. A challenging task is to identify the significant risk factors that are responsible for depression among physicians. To address this issue, the study aimed to build a machine learning-based predictive model that will be capable of predicting depression levels and finding associated risk factors. A raw dataset was collected to conduct this study and preprocessed as necessary. Then, the dataset was divided into 10 sub-datasets to determine the best possible set of attributes to predict depression. Seven different classification algorithms, KNN, DT, LGBM, GB, RF, ETC, and StackDPP, were applied to all the sub-datasets. StackDPP is a stacking-based ensemble classifier, which is proposed in this study. It was found that StackDPP outperformed on all the datasets. The findings indicate that the StackDPP with the sub-dataset with all the attributes gained the highest accuracy (0.962581), and the top 20 attributes were enough to gain 0.96129 accuracy by StackDPP, which was close to the performance of the dataset with all the attributes. In addition, risk factors were analyzed in this study to reveal the most significant risk factors that are responsible for depression among physicians. The findings of the study indicate that the proposed model is highly capable of predicting the level of depression, along with finding the most significant risk factors. The study will enable mental health professionals and psychiatrists to decide on treatment and therapy for physicians by analyzing the depression level and finding the most significant risk factors.
YOLO_CC: Deep Learning based Approach for Early Stage Detection of Cervical Cancer from Cervix Images Using YOLOv5s Model
(Daffodil International University, 2022-08-26) Ontor, Md Zahid Hasan; Ali, Md Mamun; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed; Mahmud, S. M. Hasan; Azam, Sami
Cervical Cancer (CC) is the fourth major cancer, which is responsible for a large number of deaths among women. Early stage detection of the cancer is the most effective solutions for decreasing the mortality rate. The lack of awareness, and costly clinical diagnosis are the major barrier for women to participate in clinical screening test to detect CC at an early stage. To address the issue, the study aims to find a deep learning based intelligent system to detect CC in the early stage using real time images. To build the model, cervical cancer pap-smear test image datasets were gathered and these were labeled and preprocessed. Then the YOLOv5 model was employed on the labeled dataset to train the model. Three latest versions of YOLOv5 model were applied in this study to find the most efficient model for building the intelligent system to detect CC at an early stage. All of the applied models provided satisfactory performance. Among all the applied models, YOLOv5s outperformed with 0.8279 precision and 0.8265 recall value. The performance of the study indicates that the proposed model highly potential to diagnose CC using real time images in early stage. In the medical field, the proposed will be quite useful for clinicians, and medical professionals.(CC) is the fourth major cancer, which is responsible for a large number of deaths among women. Early stage detection of the cancer is the most effective solutions for decreasing the mortality rate. The lack of awareness, and costly clinical diagnosis are the major barrier for women to participate in clinical screening test to detect CC at an early stage. To address the issue, the study aims to find a deep learning based intelligent system to detect CC in the early stage using real time images. To build the model, cervical cancer pap-smear test image datasets were gathered and these were labeled and preprocessed. Then the YOLOv5 model was employed on the labeled dataset to train the model. Three latest versions of YOLOv5 model were applied in this study to find the most efficient model for building the intelligent system to detect CC at an early stage. All of the applied models provided satisfactory performance. Among all the applied models, YOLOv5s outperformed with 0.8279 precision and 0.8265 recall value. The performance of the study indicates that the proposed model highly potential to diagnose CC using real time images in early stage. In the medical field, the proposed will be quite useful for clinicians, and medical professionals.