Download PDFOpen PDF in browserA Systematic Review on Pre-Trained Models on C-NMC Leukemia Using Deep Learning Techniques13 pages•Published: August 6, 2024AbstractThe early detection of Acute Lymphoblastic Leukemia (ALL) poses a significant chal- lenge in the medical field due to the subtle morphological features of ALL cells, which often resemble healthy cells. This necessitates the expertise of experienced hematologists, a re- liance on human interpretation that introduces subjectivity and labor-intensive processes. Consequently, timely diagnosis and treatment initiation can be hindered. By leveraging the capabilities of machine learning,the paper aim to establish a system that can accurately distinguish between healthy and ALL cells, thereby reducing the reliance on subjective human interpretation and expediting the diagnostic process. This systematic review thor- oughly examines the use of deep learning for classifying and detecting acute leukemia. This study discusses many stages such as preprocessing, augmentation, segmentation, and feature extraction that are taken before classification. It also addresses the issues faced by the authors in different datasets. This research study examined and compared several benchmark models VGG16, VGG19, Inception, Xception, Efficient NetB0, ResNet50, and ResNet101. Out of these models, ResNet101 came as the top performer with a Validation Accuracy of 76.36%, Validation Precision of 75.85%, and Validation Recall of 76.36%. This comparative analysis aims to elucidate the strengths and weaknesses of these models, contributing valuable insights.Keyphrases: deep learning, image classification, medical ai, neural networks, pattern recognition In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 475-487.
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