Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia

Nitla Gokulkrishnan; Tushar Nayak; Niranjana Sampathila

doi:10.1109/CONECCT57959.2023.10234824

Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia

Nitla Gokulkrishnan^*
, Tushar Nayak
, Niranjana Sampathila

^*Corresponding author for this work

Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350334395
DOIs	https://doi.org/10.1109/CONECCT57959.2023.10234824
Publication status	Published - 2023
Event	9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023 - Bangalore, India Duration: 14-07-2023 → 16-07-2023

Publication series

Name	Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies

Conference

Conference	9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023
Country/Territory	India
City	Bangalore
Period	14-07-23 → 16-07-23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Information Systems
Energy Engineering and Power Technology
Electrical and Electronic Engineering

Access to Document

10.1109/CONECCT57959.2023.10234824

Cite this

Gokulkrishnan, N., Nayak, T., & Sampathila, N. (2023). Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia. In Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies (Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONECCT57959.2023.10234824

Gokulkrishnan, Nitla ; Nayak, Tushar ; Sampathila, Niranjana. / Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia. Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies).

@inproceedings{d8587f4220b7467d99ed542a50185bb8,

title = "Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia",

abstract = "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.",

author = "Nitla Gokulkrishnan and Tushar Nayak and Niranjana Sampathila",

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year = "2023",

doi = "10.1109/CONECCT57959.2023.10234824",

language = "English",

series = "Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies",

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Gokulkrishnan, N, Nayak, T & Sampathila, N 2023, Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia. in Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies. Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies, Institute of Electrical and Electronics Engineers Inc., 9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023, Bangalore, India, 14-07-23. https://doi.org/10.1109/CONECCT57959.2023.10234824

Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia. / Gokulkrishnan, Nitla; Nayak, Tushar; Sampathila, Niranjana.
Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

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.

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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 -

Gokulkrishnan N, Nayak T, Sampathila N. Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia. In Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies). doi: 10.1109/CONECCT57959.2023.10234824

Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia

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