# Advances in Computer-Aided Medical Image Processing  
[PERSON] 1  
1 College of Software, Jilin University, Changchun 130012, China; [EMAIL_ADDRESS]  
[PERSON] 2  
2 College of Computer Science and Technology, Jilin University, Changchun 130012, China; [EMAIL_ADDRESS]  
Tel. : +86-13604418937  
[PERSON] 2  
1 College of Software, Jilin University, Changchun 130012, China; [EMAIL_ADDRESS]  
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images relies heavily on the experience, responsibility, and awareness of doctors, so each film needs to be carefully screened by doctors, which consumes considerable energy. Meanwhile, too much mechanical and repetitive work also causes doctors to make mistakes in judgement due to excessive fatigue. At this time, using computer technology to assist in tumor diagnosis to help doctors improve speed and reduce errors has gradually become a research hotspot. With the continuous development of medical imaging technology and computer technology and the improvement of computing power, big data technology makes the application of artificial intelligence increasingly extensive [1]. Currently, automated medical image analysis has become an indispensable technical means in medical research, clinical diagnosis, and treatment. At the same time, medical image segmentation based on deep learning has made rapid progress. Some segmentation models with good performance have been proposed successively. Deep learning algorithms, especially convolutional networks [2], have quickly become the preferred method for analyzing medical images [3,4]. Compared with the manual segmentation method, the model based on deep learning is more accurate and quicker, which can greatly reduce the work burden of clinicians. Medical image segmentation based on deep learning has become an important research branch in the field of computer vision and has been widely studied by researchers at home and abroad. In the sudden outbreak of COVID-19, the detection method of deep learning was also an important tool for medical image recognition, which helps to automatically and quickly evaluate CT images to distinguish COVID-19 from other clinical entities and plays a huge role in actual life [5, 6, 7]. At the same time, deep learning plays a key role in the identification of cervical cancer and breast cancer, a disease with high death rate among women [8, 9, 10, 11]. This paper presents a comprehensive overview of computer technology applications in medical imaging, addressing both the unique characteristics of medical images and the similarities they share with common images. This paper provides a comprehensive analysis of medical imaging techniques, specifically MRI and CT, along with their associated challenges. It discusses direct image processing methods, such as image enhancement and multimodal medical image fusion, as well as intelligent image recognition tailored to specific body parts using deep learning models such as CNNs, transfer learning, attention mechanisms, and cascading strategies. The significance of neural network design in medical imaging is emphasized, with a focus on extracting multilevel features through U-shaped structures, dense connections, 3D convolution, and multimodal feature fusion. Deeper and wider networks, smaller convolution kernels, pretraining, and feature augmentation are explored to improve network performance and efficiency. The paper also addresses the challenges in medical image recognition, such as data quality, model interpretability, generalizability, and computational resources. To overcome these obstacles, future directions include improving data accessibility, employing active learning, developing explainable AI, enhancing model robustness, and optimizing computational efficiency.