Deep learning (DL) has emerged as a promising tool for improving the diagnostic accuracy of ovarian cancer. This review explores the application of DL techniques, particularly convolutional neural networks (CNNs), in ovarian cancer diagnosis using various medical imaging modalities such as MRI, ultrasound, CT, and PET. DL models have demonstrated the ability to accurately classify ovarian tissues and achieve diagnostic performance comparable to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the potential to enhance patient outcomes, refine treatment approaches, and support informed decision-making. However, further research is needed to ensure the reliability and applicability of DL models in clinical settings. The review highlights the potential of DL in accelerating the diagnostic process and offering more precise and efficient solutions. DL models have been developed to classify ovarian tissues as malignant, borderline, benign, or normal based on second-harmonic generation imaging, achieving high areas under the receiver operating characteristic curve. A preliminary study comparing DL and radiologist assessments for diagnosing ovarian carcinoma using MRI demonstrated that DL exhibited non-inferior diagnostic performance compared to experienced radiologists. Various DL techniques have been applied to ovarian cancer diagnosis, including U-Net, ResNet, and YOLOv5. These models have shown high accuracy, precision, recall, and F1 scores in differentiating between benign and malignant ovarian tumours. For example, a study using a multi-modal evolutionary DL model achieved a precision of 98.76%, recall of 98.74%, accuracy of 98.87%, and F1 score of 99.43%. Another study using a deep hybrid learning model achieved a training and validation AUC score of 0.99 and a test AUC score of 1.00. DL has also been applied to differentiate between different histologic subtypes of ovarian cancer, such as mucous, serous, endometroid, and clear cell carcinomas. The integration of DL with radiomics has shown promise in improving the accuracy of ovarian cancer diagnosis. Radiomics involves the extraction of quantitative characteristics from medical images, which can be used to characterize the tumour phenotype and predict patient outcomes. DL models have been developed to combine radiomics features with deep learning techniques, achieving better classification performance. Overall, DL has shown great potential in improving the diagnostic accuracy of ovarian cancer. However, further research is needed to ensure the reliability and applicability of DL models in clinical settings. The integration of DL into ovarian cancer diagnosis holds the potential to enhance patient outcomes, refine treatment approaches, and support informed decision-making.Deep learning (DL) has emerged as a promising tool for improving the diagnostic accuracy of ovarian cancer. This review explores the application of DL techniques, particularly convolutional neural networks (CNNs), in ovarian cancer diagnosis using various medical imaging modalities such as MRI, ultrasound, CT, and PET. DL models have demonstrated the ability to accurately classify ovarian tissues and achieve diagnostic performance comparable to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the potential to enhance patient outcomes, refine treatment approaches, and support informed decision-making. However, further research is needed to ensure the reliability and applicability of DL models in clinical settings. The review highlights the potential of DL in accelerating the diagnostic process and offering more precise and efficient solutions. DL models have been developed to classify ovarian tissues as malignant, borderline, benign, or normal based on second-harmonic generation imaging, achieving high areas under the receiver operating characteristic curve. A preliminary study comparing DL and radiologist assessments for diagnosing ovarian carcinoma using MRI demonstrated that DL exhibited non-inferior diagnostic performance compared to experienced radiologists. Various DL techniques have been applied to ovarian cancer diagnosis, including U-Net, ResNet, and YOLOv5. These models have shown high accuracy, precision, recall, and F1 scores in differentiating between benign and malignant ovarian tumours. For example, a study using a multi-modal evolutionary DL model achieved a precision of 98.76%, recall of 98.74%, accuracy of 98.87%, and F1 score of 99.43%. Another study using a deep hybrid learning model achieved a training and validation AUC score of 0.99 and a test AUC score of 1.00. DL has also been applied to differentiate between different histologic subtypes of ovarian cancer, such as mucous, serous, endometroid, and clear cell carcinomas. The integration of DL with radiomics has shown promise in improving the accuracy of ovarian cancer diagnosis. Radiomics involves the extraction of quantitative characteristics from medical images, which can be used to characterize the tumour phenotype and predict patient outcomes. DL models have been developed to combine radiomics features with deep learning techniques, achieving better classification performance. Overall, DL has shown great potential in improving the diagnostic accuracy of ovarian cancer. However, further research is needed to ensure the reliability and applicability of DL models in clinical settings. The integration of DL into ovarian cancer diagnosis holds the potential to enhance patient outcomes, refine treatment approaches, and support informed decision-making.