Deep Learning-Based Ovarian Cancer Subtype Classification Using VGG16 and MobileNetV2 with Squeeze-and-Excitation Blocks

Salman Mohammad; Abdullah Al; Nayem Uddin; Labonno Akter

doi:10.25163/angiotherapy.889898

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

RESEARCH ARTICLE (Open Access)

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Deep Learning-Based Ovarian Cancer Subtype Classification Using VGG16 and MobileNetV2 with Squeeze-and-Excitation Blocks

Salman Mohammad Abdullah¹, Abdullah Al Masum², Nayem Uddin Prince³, Labonno Akter Mim⁴

+ Author Affiliations

Journal of Angiotherapy 8 (8) 1-11 https://doi.org/10.25163/angiotherapy.889898

Submitted: 03 June 2024 Revised: 11 August 2024 Published: 15 August 2024

Abstract

Background: Ovarian carcinoma remains one of the deadliest gynecological malignancies due to its heterogeneity and late-stage detection. Accurate classification of its subtypes—High-Grade Serous Carcinoma (HGSC), Clear-Cell Carcinoma (CC), Endometrioid Carcinoma (EC), Low-Grade Serous Carcinoma (LGSC), and Mucinous Carcinoma (MC)—is essential for tailored treatments, yet traditional histopathological methods often lack precision. This study aimed to develop a deep learning (DL) model to enhance ovarian cancer subtype classification using histopathological images. Methods: Histopathological images were collected from medical repositories, and data augmentation techniques were applied to increase dataset diversity. Two convolutional neural network (CNN) architectures, VGG16 and MobileNetV2, were fine-tuned using transfer learning to classify the subtypes. The models were pre-trained on the ImageNet dataset and evaluated through accuracy, precision, recall, F1-score, and ROC-AUC, with K-fold cross-validation ensuring robustness. Results: Results indicated that VGG16 improved classification over baseline CNN models, while MobileNetV2, with Squeeze-and-Excitation (SE) blocks, achieved the highest performance, offering greater accuracy and computational efficiency. MobileNetV2’s lightweight architecture captured intricate tissue patterns more effectively, making it the superior model. Conclusion: This study highlights the potential of advanced DL models, particularly MobileNetV2 with SE block attention, for improving ovarian cancer subtype classification. These findings offer promising implications for clinical practice and personalized treatment approaches. Future research should focus on larger datasets and integrating multimodal data for further advancements.

Keywords: Ovarian carcinoma, Deep learning, Convolutional neural networks, MobileNetV2, Transfer learning

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