Deep Learning Based Ovarian Cancer Classification Using EfficientNetB2 with Attention Mechanism
Abstract
Ovarian cancer is a gynecological malignancy comprising multiple histopathological subtypes. Traditional diagnostic tools like histopathology and CA-125 tests suffer from limitations, including inter-observer variability, low specificity, and time-consuming procedures, often leading to delayed or incorrect diagnoses, which are subjective and error-prone. Conventional machine learning models, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), have been applied but often struggle with high-dimensional image data and fail to extract deep morphological features. This study proposes a DL-based framework to classify ovarian cancer subtypes from histopathological images, aiming to enhance diagnostic accuracy and clinical decision-making. Initially, Deep learning was applied using pre-trained architectures such as VGG-16, Xception, and EfficientNetB2. However, the standout innovation in this study is the integration of EfficientNetB2 with Convolutional Block Attention Module (CBAM), an attention mechanism module. An attention mechanism allows the model to focus on the most informative regions of the image, thereby improving diagnostic precision. The proposed system was trained and validated on a diverse, well-structured dataset, achieving high accuracy and strong generalization capability. EfficientNetB2 with CBAM outperformed other models, achieving a 91% accuracy rate compared to 52% for VGG-16, 72% for Xception, and 82% for the baseline EfficientNetB2 model. This attention-enhanced, scalable AI model demonstrates strong potential for clinical application. It provides faster and more efficient classification of ovarian cancer subtypes compared to conventional approaches. The framework has the potential to improve survival outcomes for patients with ovarian cancer. The proposed system demonstrates a significant improvement in ovarian cancer subtype classification (High-Grade Serous Carcinoma, Low-Grade Serous Carcinoma, Clear-Cell, Endometrioid, and Mucinous Carcinoma). It provides a practical tool for aiding early diagnosis and treatment planning, with potential for integration into clinical workflows.
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