Explainable Transfer Learning for Histopathological Image Classification of Ovarian Cancer

Timely diagnosis and effective treatment of ovarian cancer depend heavily on accurate subtype classification. This study presents a deep learning model that utilizes transfer learning with a customized DenseNet201 architecture. Several subtypes of ovarian cancer are classified by the proposed model using histopathology images. Two balanced, openly accessible datasets are used for training and validation. In order to enhance generalization and avoid overfitting, data augmentation approaches are used during preprocessing. Accuracy and AUC are two of the measures used to assess the performance of the model. Interpretability is improved through the use of Local Interpretable Model-agnostic Explanations (LIME). LIME offers graphical representations of the model's decision-making procedure. For clinical applications where transparency is required, this capability is essential. On each dataset, experimental results demonstrate that the proposed model performs better than the state-of-the-art approaches currently in use. Every metric show that it performs better in classification. Even with variable input, the model exhibits good robustness and steady accuracy. The efficacy of transfer learning and the prospect of explainable AI in medical image classification are demonstrated by these findings. In particular, it emphasizes how useful it is for diagnosing ovarian cancer. The results highlight how combining explainable AI and deep learning methods can yield dependable, scalable solutions for precise and early ovarian cancer subtype diagnosis. This may increase the likelihood of timely diagnosis and individualized treatment regimens. Additionally, the finding opens the door for further research into healthcare technology and automated diagnostic tools for oncology.