Analysis of Acute Lymphoblastic Leukemia Detection Methods Using Deep Learning
Authors:
- 18 October 2023
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 565-574
Abstract
This research work puts forward a comparative study of four prominent deep learning models - ResNet, InceptionNet, MobileNet and EfficientNet — for the classification and detection of Acute Lymphoblastic Leukemia (ALL) from microscopic single blood cell images. Leukemia, a critical hematological malignancy, demands accurate and swift diagnosis to facilitate effective treatment. The advent of deep learning has revolutionized medical image analysis, enabling automated and efficient disease detection. In this work, we evaluate the performance of ResNet, InceptionNet, MobileNet, and EfficientNet, all of which have demonstrated exceptional capabilities in various computer vision tasks. The proposed study involves the construction of a dataset containing diverse blood cell images, which then undergoes preprocessing and augmentation to ensure model robustness and generalization. Subsequently, the four deep learning architectures are implemented, pretrained on large-scale image datasets, and fine-tuned on the leukemia dataset. Training, validation, and testing phases are conducted under controlled experimental conditions. The results reveal nuanced differences in the performance of ResNet, InceptionNet, MobileNet, and EfficientNet for leukemia detection and classification. The evaluation metrics provide insights into their strengths and limitations, helping guide selection based on specific application requirements. This study clarifies how various architectures impact model performance in the context of medical image analysis.Keywords
This publication has 20 references indexed in Scilit:
- Diagnosis of Leukemia Disease through Deep Learning using Microscopic Images2022
- A Transfer Learning-Based Deep CNN Approach for Classification and Diagnosis of Acute Lymphocytic Leukemia Cells2022
- Multi Class Classification of Acute Leukemia using Transfer Learning2022
- Feature Fusion based Deep Learning method for Leukemia cell classification2021
- Image Analysis of Cells Acute Lymphoblastic Leukemia Using Ensemble Learning of Deep Bagging2021
- Transfer Learning-Based Automatic Detection of Acute Lymphocytic Leukemia2021
- Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning2021
- Accurate Recognition of Leukemia Sub-types by Utilizing a Transfer Learned Deep Convolutional Neural Network2021
- Leukemia Diagnosis Based on Machine Learning Algorithms2020
- Deep-Learning-Based Acute Leukemia Classification Using Imaging Flow Cytometry and Morphology2018