TY - GEN
T1 - Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia
AU - Gokulkrishnan, Nitla
AU - Nayak, Tushar
AU - Sampathila, Niranjana
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Leukemia, a type of cancer affecting the blood and bone marrow, involves the abnormal production of leukocytes and can impact the immune system. While more prevalent among children, it can also affect adults. Early detection plays a critical role in effective treatment and patient recovery. In this paper, we have used an open source four-class Acute Lymphoblastic Leukemia (ALL) dataset that has been segmented using color thresholding. Subsequently, these images have then been trained on pre-trained Convolutional Neural Networks (CNNs): ResNet-50 and ResNet-101, with hyperparameter tuning to classify between benign and three stages of malignant ALL lymphoblast cells. The results demonstrate that our proposed method achieved accuracies exceeding 98% in detecting ALL, indicating the potential of deep learning-based classifiers in aiding hematologists accurately detect ALL and improving patient outcomes.
AB - Leukemia, a type of cancer affecting the blood and bone marrow, involves the abnormal production of leukocytes and can impact the immune system. While more prevalent among children, it can also affect adults. Early detection plays a critical role in effective treatment and patient recovery. In this paper, we have used an open source four-class Acute Lymphoblastic Leukemia (ALL) dataset that has been segmented using color thresholding. Subsequently, these images have then been trained on pre-trained Convolutional Neural Networks (CNNs): ResNet-50 and ResNet-101, with hyperparameter tuning to classify between benign and three stages of malignant ALL lymphoblast cells. The results demonstrate that our proposed method achieved accuracies exceeding 98% in detecting ALL, indicating the potential of deep learning-based classifiers in aiding hematologists accurately detect ALL and improving patient outcomes.
UR - https://www.scopus.com/pages/publications/85172685068
UR - https://www.scopus.com/inward/citedby.url?scp=85172685068&partnerID=8YFLogxK
U2 - 10.1109/CONECCT57959.2023.10234824
DO - 10.1109/CONECCT57959.2023.10234824
M3 - Conference contribution
AN - SCOPUS:85172685068
T3 - Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies
BT - Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023
Y2 - 14 July 2023 through 16 July 2023
ER -
