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All Journal International Journal of Electrical and Computer Engineering
Chakraborty, Narayan Ranjan
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Computer Science & IT Electrical & Electronics Engineering

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Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization Hridoy, Rashidul Hasan; Arni, Arindra Dey; Ghosh, Shomitro Kumar; Chakraborty, Narayan Ranjan; Mahmud, Imran
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2181-2190

Abstract

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.
A light-weight and generalizable deep learning model for the prediction of COVID-19 from chest X-ray images Zobair, Md Jakaria; Orpa, Refat Tasfia; Ashef, Mahir; Siddiquee, Shah Md Tanvir; Chakraborty, Narayan Ranjan; Habib, Ahsan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4068-4077

Abstract

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.
Co-Authors Ahsan Habib, Ahsan Arni, Arindra Dey Ashef, Mahir Ghosh, Shomitro Kumar Hridoy, Rashidul Hasan Mahmud, Imran Orpa, Refat Tasfia Siddiquee, Shah Md Tanvir Zobair, Md Jakaria