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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Zaid Bin Sajid
Southeast University

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Comparative deep learning CNN architectures for breast cancer detection from thermal imaging Md. Sumon Hosen; Mustafizur Rahman; Zaid Bin Sajid; Md Naeem Hossan; Apu Biswas; Md. Mijanur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp369-379

Abstract

It has been observed that breast cancer is a severe disease among women globally. Mammography is the most effective screening method for detecting this severe illness. Over the last thirty years, mammography has been widely recognized as a preventive measure against breast cancer. In recent years, convolutional neural networks (CNN) and artificial intelligence (AI) have become more common in digital mammography for automated breast cancer detection. For classifying breast cancer, this study examines the five CNN models: LeNet-5, AlexNet, VGG-16, ResNet-50, and Inception-v3, using the database for mastology research with infrared images (DMR-IR) dataset's thermal image. These models were trained and validated using accuracy, recall, F1-score, specificity, and AUC as evaluation criteria after the dataset was preprocessed using normalization and data augmentation. Among the experimental models, Inception-v3 achieved 99.44% accuracy, outperforming other CNNs by 1–2%, while other models performed with accuracy levels above 97%. These results show the tremendous efficacy of CNN-based deep learning methods for breast thermogram analysis. The research points out thermography as a useful support for traditional imaging and InceptionV3 as a potential option for correctly detecting clinical breast cancer.
A multi-class classification approach for feminist sentiment analysis in Bangla social media using TF-IDF and ensemble learning Zaid Bin Sajid; Md. Mijanur Rahman; Md. Sumon Hosen; Sarara Jaman Riya; Yeamin Akon; S. M. Fahad Bin Jim; Ornab Biswass
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp584-595

Abstract

Social media has emerged as an important part of societal discourse on feminism and gender equality, especially in Bangladesh. Nevertheless, any feminist debate on social media in Bengali polarizes reactions, highlighting the need for automated sentiment analysis. This paper introduces one of the earliest multi-class feminist sentiment classification schemes of the Bengali social media with a manually annotated dataset of 6,830 comments categorized as positive, neutral, or negative. The framework uses term frequency-inverse document frequency (TF-IDF) based n-gram feature representations utilizing traditional machine learning algorithms, with a majority voting ensemble to determine optimal robust models. The data was divided into 80% and 20% for training and testing, respectively. Models were evaluated on the basis of accuracy, precision, recall, and macro-F1 to correct on imbalance of classes. Multinomial naive bayes (MNB) has the best accuracy of 84.74% and macro-F1 of 84.66, which is 4-7 times higher than other models. The ensemble method improved feature strength. Such results indicate that lightweight machine learning models based on TF-IDF features and ensemble models can be useful to detect feminist sentiment in Bangla social media and serve as a guideline in the field of domain-specific sentiment analysis in low-resource languages and help monitor online feminist discourse.
Co-Authors Apu Biswas Md Naeem Hossan Md. Mijanur Rahman Md. Sumon Hosen Mustafizur Rahman Ornab Biswass S. M. Fahad Bin Jim Sarara Jaman Riya Yeamin Akon