The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase.

We presented a methodology for detecting acute lymphoblastic leukemia (ALL) based on image data. The approach involves two stages: Feature extraction and classification. Three state-of-the-art transfer learning models, InceptionResnetV2, Densenet121, and VGG16, were utilized to extract features from the images. The extracted features were then processed through a Global Average Pooling layer and concatenated into a flattened tensor. A linear support vector machine (SVM) classifier was trained and tested on the resulting feature set. Performance evaluation was conducted using metrics such as precision, accuracy, recall, and F-measure. The experimental results demonstrated the efficacy of the proposed approach, with the highest accuracy achieved at 91.63% when merging features from VGG16, InceptionResNetV2, and DenseNet121. We contributed to the field by offering a robust methodology for accurate classification and highlighted the potential of transfer learning models in medical image analysis. The findings provided valuable insights for developing automated systems for the early detection and diagnosis of leukemia. Future research can explore the application of this approach to larger datasets and extend it to other types of cancer classification tasks.