The paper provides a comprehensive review of deep convolutional neural networks (CNNs) in medical image analysis. It highlights the advancements in biomedical engineering that have driven the use of machine learning techniques, particularly deep learning, for analyzing medical images. The review covers various applications of CNNs, including segmentation, abnormality detection, disease classification, computer-aided diagnosis (CAD), and image retrieval. Key performance parameters such as accuracy, F1-score, precision, recall, sensitivity, and specificity are discussed, along with the challenges and potential of these techniques. The paper also explores different CNN architectures, their effectiveness in handling 3D imaging modalities, and the limitations of deep learning in the clinical domain. Finally, it concludes by emphasizing the growing acceptance of CNN-based methods in medical image analysis and the need for further research to expand their application to new imaging modalities.The paper provides a comprehensive review of deep convolutional neural networks (CNNs) in medical image analysis. It highlights the advancements in biomedical engineering that have driven the use of machine learning techniques, particularly deep learning, for analyzing medical images. The review covers various applications of CNNs, including segmentation, abnormality detection, disease classification, computer-aided diagnosis (CAD), and image retrieval. Key performance parameters such as accuracy, F1-score, precision, recall, sensitivity, and specificity are discussed, along with the challenges and potential of these techniques. The paper also explores different CNN architectures, their effectiveness in handling 3D imaging modalities, and the limitations of deep learning in the clinical domain. Finally, it concludes by emphasizing the growing acceptance of CNN-based methods in medical image analysis and the need for further research to expand their application to new imaging modalities.