Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Niranjana Sampathila, Krishnaraj Chadaga, Neelankit Goswami, Rajagopala P. Chadaga, Mayur Pandya, Srikanth Prabhu, Muralidhar G. Bairy, Swathi S. Katta, Devadas Bhat, Sudhakara P. Upadya

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.

Original languageEnglish
Article number1812
JournalHealthcare (Switzerland)
Issue number10
Publication statusPublished - 10-2022

All Science Journal Classification (ASJC) codes

  • Leadership and Management
  • Health Policy
  • Health Informatics
  • Health Information Management


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