A cross-validated deep learning framework for automated detection of DMBA-induced ovarian cancer from...

Ovarian cancer (OC) remains a major global health challenge due to its asymptomatic progression and the lack of reliable diagnostic tools. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance diagnostic accuracy through automated analysis of biomedical images and integration of multimodal data. Female albino rats were divided into control and cancer-induced groups. OC were chemically induced by 7,12-Dimethylbenz[a]anthracene (DMBA) exposure. Blood samples were collected at baseline and predefined post-induction time points to quantify serum Cancer Antigen 125 (CA-125) levels, which were significantly elevated in the cancer group (438.7 ± 6.4 U/mL) compared with controls (22.5 ± 3.1 U/mL; p < 0.01). Ovarian tissues were harvested, processed, and imaged for high-resolution histopathological analysis. A custom convolutional neural network (CNN) was developed to classify ovarian tissue images into normal and cancerous categories. The dataset consisted of 904 labeled images and was evaluated using stratified data splitting and 5-fold cross-validation. The proposed CNN achieved a test accuracy of 96.69%, precision of 95.8%, recall of 97.2%, and F1-score of 96.5%. Cross-validation further demonstrated robust and stable performance with a mean accuracy of 96.42 ± 0.52%. Comparative benchmarking showed superior performance over conventional machine learning methods and transfer learning models, including Support Vector Machine, Random Forest, MobileNetV2, ResNet50, and EfficientNetB0. These findings indicate that the integration of serum biomarkers, histopathology, and deep learning provides a robust preclinical framework for ovarian cancer detection. This AI-assisted diagnostic strategy demonstrates strong translational potential for improving early diagnosis, risk stratification, and clinical decision support in ovarian cancer.