Deep Learning Technique for Interpretable Diagnosis of Polycystic Ovary Syndrome in Ultrasound Imaging

Polycystic Ovary Syndrome (PCOS) is a widespread condition in women that is associated with hormonal disorders, absentee or infrequent menstruation, and cyst formation on ovaries. The diagnostic process of PCOS is still difficult because of ambiguity in ultrasound images. Earlier approaches emphasize the detection and classification of follicles for ultrasound images and disregard interpretability, which obscures understanding the model’s decisions and trustworthiness in critical medical applications. This research proposes a method of diagnosing PCOS in two stages. Initially, follicles are located with a YOLOv8 model that has multiscale feature dose attention. Follicles are subsequently classified with a ResNet50 model that has SE blocks. The last step is for Grad-CAM to show which features of the image were used by the classification model in order to explain its decision and provide meaningful insights regarding the model's predictions. Evaluation was performed on two publicly available datasets. The proposed method outperforms all other methods in follicle detection with 92% mean average precision (mAP) and classification accuracies of 98.25% and 94.56% on datasets 1 and 2, respectively. This result makes the proposed model a reliable and transparent technique for robust clinical applications. By providing a scalable, deployable, and interpretable diagnostic pipeline that can be integrated into AI-based health-tech platforms, the suggested method also fits with the objectives of technopreneurship incubator models.