Advancing Ovarian Cancer Diagnosis Through Deep Learning and Explainable AI: A Multiclassification Approach

Aim:

        To develop a robust and interpretable AI system for ovarian cancer diagnosis using multiclassification techniques and advanced deep learning models, including ResNet152V2, EfficientNetV2B3, and ResNet50V2.

Abstract:

          Ovarian cancer poses a severe diagnostic challenge due to its heterogeneous subtypes and the lack of effective early detection methods. This study introduces a novel AI-driven diagnostic framework leveraging state-of-the-art deep learning architectures—ResNet152V2, EfficientNetV2B3, and ResNet50V2—for multiclass classification of ovarian cancer subtypes. Enhanced by Explainable AI (XAI) techniques, such as LIME and saliency maps, the system not only achieves high diagnostic accuracy but also provides interpretable visualizations for clinical decision-making. Initial evaluations demonstrate significant performance improvements, achieving precision, recall, and F1-scores surpassing conventional diagnostic methods. By combining cutting-edge deep learning with explainable methodologies, the proposed system aims to revolutionize ovarian cancer diagnosis, fostering trust, and adoption in clinical practice.

Introduction:

           Ovarian cancer remains one of the deadliest gynecological malignancies, with a global mortality rate exacerbated by late-stage diagnoses and the absence of reliable screening tools. Its complex histological subtypes, including serous, endometroid, mucinous, and clear cell carcinomas, further complicate early detection. While traditional diagnostic methods such as transvaginal ultrasound and CA-125 serum markers provide limited accuracy, advancements in AI, particularly deep learning, offer new hope. This paper explores the potential of combining advanced deep learning architectures with explainable AI techniques to enhance diagnostic precision and clinical interpretability.

Existing System:

          Existing diagnostic methods for ovarian cancer rely heavily on imaging techniques like transvaginal ultrasound, MRI, and serum marker analysis (e.g., CA-125). These approaches are often hampered by limited sensitivity and specificity, particularly in detecting early-stage cancers. Traditional AI models, such as basic convolutional neural networks (CNNs), have shown promise but fail to incorporate interpretability, limiting their clinical adoption. Moreover, current systems often lack multiclass classification capabilities, reducing their utility in differentiating among cancer subtypes.

Problem Definition:

          Ovarian cancer diagnosis presents significant challenges due to the complexity and diversity of its subtypes. Existing diagnostic tools, such as imaging techniques and serum marker analysis, often lack sensitivity and specificity, especially for early-stage detection. Traditional AI-based solutions have demonstrated potential, but their inability to handle multiclass classification and provide interpretability hinders their adoption in clinical settings. Additionally, distinguishing between closely related histological subtypes remains a daunting task due to overlapping features, further complicating the development of precise diagnostic methods. Addressing these issues requires a robust system that combines advanced diagnostic accuracy with explainable insights for clinical usability.

Proposed System:

     The proposed system utilizes advanced deep learning architectures, including ResNet152V2, EfficientNetV2B3, and ResNet50V2, to address the challenges of ovarian cancer diagnosis. By leveraging these models’ superior feature extraction and classification capabilities, the system performs multiclass classification to differentiate between cancerous, precancerous, and non-cancerous tissues. Furthermore, the integration of Explainable AI (XAI) techniques, such as LIME and saliency maps, ensures that the system provides interpretable visualizations, fostering trust and usability in clinical environments. Robust data augmentation methods enhance the system’s performance, while a comparative analysis identifies the most effective architecture for deployment.

Advantages:

1. High diagnostic accuracy across ovarian cancer subtypes.
2. Real-time, interpretable visualizations for clinical decision support.
3. Scalable and efficient models suitable for integration into clinical workflows.
4. Improved trust and adoption due to enhanced model transparency.