Attention Based Feature Fusion with External Attention Transformers for Ovarian Cancer Histopathology Analysis

Ovarian cancer is a deadly gynecological cancer, and diagnosing it can be quite challenging due to its several variants. Generally speaking, the high classification accuracy leads to a good prognosis and efficient therapy. Traditional diagnosis methods rely on the subjective and time-consuming histopathological analysis of Ovarian tissue samples. In order to overcome the drawbacks of human diagnosis, the problem statement centres on creating a computational model that can automatically categorize histopathological images into benign or malignant groups. The ability of Convolutional Neural Networks (CNNs) to automatically learn and extract features makes them excellent in picture identification. They are very good at tasks like object detection and facial recognition because of their architecture, which imitates how the human brain processes visual information. These methods may, however, be susceptible to overfitting and require a large amount of processing power because to their complex architecture. In order to provide a method for classifying images of Ovarian cancer, this work aims to present an External Attention Transformer (EAT) model that makes use of external attention mechanisms. Metrics like accuracy, precision, recall, and F1-score are essential for assessing how well machine learning models perform. They shed light on how well the model works, particularly in balancing false positives with genuine positives to guarantee high accuracy and computational efficiency.