Deep learning for cancer classification, diagnosis, and treatment response prediction

Deep learning has emerged as a powerful tool in medical sciences, offering innovative solutions for cancer diagnosis, classification, and treatment response prediction. This review provides an overview of the applications of deep learning in oncology, focusing on its impact on improving accuracy and efficiency in cancer-related tasks. The use of deep learning tools, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has enabled significant advancements in medical imaging analysis. These tools have shown remarkable performance in classifying cancerous lesions in various imaging modalities, including MRI, CT, and PET scans. Furthermore, deep learning models have been instrumental in predicting treatment outcomes for cancer patients. By analyzing patient data, including genetic information and treatment histories, these models can help clinicians personalize treatment plans and improve patient outcomes. Overall, this review highlights the potential of deep learning in transforming cancer care. By leveraging the power of deep learning, healthcare professionals can enhance their decision-making processes and provide more effective treatments for cancer patients..

Keywords:

deep learning , cancer , medical imaging , classification , diagnosis , treatment response prediction , convolutional neural networks , personalized medicine.