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All Journal International Journal of Electrical and Computer Engineering
Haimoudi, El Khatir
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Computer Science & IT Electrical & Electronics Engineering

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Optimizing breast cancer diagnosis: combining hybrid architectures through Apache Spark Taib, Chaymae; Abdoun, Otman; Haimoudi, El Khatir
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.pp4261-4272

Abstract

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.
Improving breast cancer classification with a novel VGG19-based ensemble learning approach Taib, Chaymae; Ahmadi, Adnan El; Abdoun, Otman; Haimoudi, El Khatir
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2809-2819

Abstract

Breast cancer is one of the most life-threatening diseases, particularly affecting women, highlighting the importance of early detection for improving survival rates. In this study, we propose a novel diagnostic framework that combines a modified VGG19 architecture with Bagging ensemble learning, using three base classifiers: decision tree (DT), logistic regression (LR), and support vector machine (SVM). We also compare this approach with twenty-four hybrid models, integrating various convolutional neural network (CNN) architectures (ResNet50, VGG19, ConvNextBase, DenseNet121, EfficientNetV2B0, EfficientNetB0, MobileNet, and NasNetMobile) with Bagging ensemble methods. Our results show that the proposed model outperforms all other architectures, especially when combined with SVM, achieving accuracy of 97% on the fine needle aspiration cytology (FNAC) dataset and 90% on the International Conference on Image Analysis and Recognition (ICIAR) dataset. This framework demonstrates strong potential for improving early breast cancer diagnosis.
Co-Authors Abdoun, Otman Ahmadi, Adnan El Taib, Chaymae