Leveraging Deep Learning For Ovarian Cancer Classification Using Image Data

Abstract

Ovarian cancer presents a significant challenge to global healthcare, demanding accurate diagnosis and specialized treatment plans. Leveraging advancements in deep learning, particularly in medical imaging, offers a promising approach to enhance subtype classification. This study introduces a robust deep learning framework for precise categorization of ovarian cancer subtypes from image data. Driven by the urgent need to improve diagnostic accuracy, our system integrates deep learning methods with convolutional neural networks (CNNs) to deliver reliable classification outcomes. Through preprocessing of image data and utilization of pre-trained CNN models for feature extraction, complex patterns corresponding to various ovarian cancer subtypes are identified. Classification is further enhanced through fine-tuning and ensemble learning techniques to maximize performance. The proposed framework addresses critical gaps in current diagnostic methods, providing clinicians with a rapid and accurate tool for subtype classification. Rigorous experimentation and validation aim to develop a system capable of generalizing across diverse patient populations, ultimately improving patient outcomes in ovarian cancer treatment. Experimental results demonstrate that our proposed model achieves a significant accuracy of 96%, along with 96% precision, recall, and F1-score, and a 97% area under the ROC curve (AUC-ROC) score. While our model's accuracy surpasses that of traditional transfer learning models such as VGG16, VGG19, Xception, and EfficientNetB4, further refinements are essential to maximize its potential in clinical applications. This study underscores the importance of advancing medical research through data-driven approaches and highlights the critical role of ongoing progress in healthcare.