DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16191-2.pdf
Reference70 articles.
1. Abdeldaim AM, Sahlol AT, Elhoseny M et al (2018) Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis, Springer International Publishing, Cham, pp 131–147. https://doi.org/10.1007/978-3-319-63754-97
2. Abhishek A, Jha RK, Sinha R et al (2022) Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques. Biomedical Signal Processing and Control 72:103341. https://doi.org/10.1016/j.bspc.2021.103341
3. Abhishek A, Jha RK, Sinha R et al (2023) Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by grad-CAM visualization. Biomed Signal Process Control 83:104722. https://doi.org/10.1016/j.bspc.2023.104722
4. Ahmed N, Yigit A, Isik Z et al (2019) Identification of leukemia subtypes from microscopic images using convolutional neural network. Diagnostics (Basel) 9(3):104
5. Ali K, Shaikh ZA, Khan AA, Laghari AA (2022) Multiclass skin cancer classification using EfficientNets–a first step towards preventing skin cancer. Neurosci Inform 2(4):100034
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