Browsing by Author "Ashef, Mahir"

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A light-weight and generalizable deep learning model for the prediction of COVID-19 from chest X-ray images
(Scopus, 2024) Zobair, Md Jakaria; Orpa, Refat Tasfia; Ashef, Mahir; Siddiquee, Shah Md Tanvir; Chakraborty, Narayan Ranjan; Habib, Ahsan
The detection of coronavirus disease (COVID-19) using standard laboratory tests, such as reverse transcription polymerase chain reaction (RT-PCR), is time-consuming. Complex medical imaging problems are currently being solved using machine learning and deep learning techniques. Our proposed solution utilizes chest radiography imaging techniques, which have shown to be a faster alternative for detecting COVID-19. We present an efficient and lightweight deep learning architecture for identifying COVID-19 using chest X-ray images which achieve 99.81% accuracy in intra-database testing and 100% accuracy in cross-validation testing on a separate data set. The results demonstrate the potential of our proposed model as a reliable tool for COVID-19 diagnosis using chest X-ray images, which can have a significant impact on improving the efficiency of COVID-19 diagnosis and treatment.
A Light-weight and Generalizable Deep Learning Model for the Prediction of Covid-19 from Chest X-ray Images
(Elsevier, 2024-08-15) Zobair, Md Jakaria; Orpa, Refat Tasfia; Ashef, Mahir; Siddiquee, Shah Md Tanvir; Chakraborty, Narayan Ranjan; Habib, Ahsan
The detection of coronavirus disease (COVID-19) using standard laboratory tests, such as reverse transcription polymerase chain reaction (RT-PCR), is time-consuming. Complex medical imaging problems are currently being solved using machine learning and deep learning techniques. Our proposed solution utilizes chest radiography imaging techniques, which have shown to be a faster alternative for detecting COVID-19. We present an efficient and lightweight deep learning architecture for identifying COVID-19 using chest X-ray images which achieve 99.81% accuracy in intra-database testing and 100% accuracy in cross-validation testing on a separate data set. The results demonstrate the potential of our proposed model as a reliable tool for COVID-19 diagnosis using chest X-ray images, which can have a significant impact on improving the efficiency of COVID-19 diagnosis and treatment.
Ensemble Based Machine Learning Model for Early Detection of Mother's Delivery Mode
(IEEE, 2023-04-19) Hasan, Mahmudul; Zobair, Md Jakaria; Akter, Sumya; Ashef, Mahir; Akter, Nazrin; Sadia, Nahid Binte
The mother's mode of delivery greatly impacts the relationship between the newborn baby and the mother, as well as the mother's and baby's health. Currently, the cesarean rate is increasing at an alarming rate. The inability to predict the mother's health status and mode of delivery are mainly responsible for this situation. Support Vector Machine (SVM), Decision Tree, Random Forest (RF), Gradient Boosting Classifier(GBC), Logistic Regression, Gaussian Naive Bayes, Stochastic Gradient Descent, CatBoost (CB), Adaptive Boosting (AB), Gaussian Naïve Bayes, Extreme Gradient Boosting(XGB) are used to predict the mother's mode of delivery. This study also proposed an ensemble machine learning algorithm that stacked the SVC, XGB, and RF together and named the ensemble SVXGBRF. To preprocess the dataset, we use a pipeline that basic preprocessing techniques, data balancing and feature selection. Our proposed SVXGBRF classifiers show 95.52% accuracy, 96% precision, recall, f1 score, and 99% AUC score. SVXGBRF shows its superiority, where most models show an accuracy of less than 90% except RF, GBC, CB, and AB. Eventually, this research could be utilized to develop a decision-support system for reducing the number of cesarean sections by trying to extract insights from complex data patterns.