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An Improved Deep Convolutionary Neural Network for Bone Marrow Cancer Detection Using Image Processing Publisher



Ramasamy MD1 ; Dhanaraj RK2 ; Pani SK3 ; Das RP4 ; Movassagh AA5 ; Gheisari M7, 9, 10 ; Liu Y7, 8 ; Porkar P6 ; Banu S2
Authors
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Authors Affiliations
  1. 1. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, India
  2. 2. School of Computing Science and Engineering, Galgotias University, India
  3. 3. Krupajal Engineering College, Biju Patnaik University of Technology, Odisha, India
  4. 4. Department of Computer Science and Engineering, CV Raman Global University, Bhubaneswar, India
  5. 5. Department of Biomedical Engineering, School of Medicine, Tehran University of medical sciences, Tehran, Iran
  6. 6. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
  7. 7. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
  8. 8. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, China
  9. 9. Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
  10. 10. Young researchers and elite club, Islamic Azad University, Parand, Iran

Source: Informatics in Medicine Unlocked Published:2023


Abstract

Bone Marrow Cancer is a type of cancer that develops in the stem cells of the bone marrow that are responsible for blood formation. AML(Acute Myeloid Leukaemia) and MM(Multiple Myeloma) are both types of malignancy that can affect bone marrow. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. Hence, in order to provide accurate treatment, a better diagnosis technique is required. In this regard, the suggested study has improved a unique technique, specifically classification and segmentation, allowing for the identification of more intricate disorders. The study's findings on the accuracy of cancer cell identification, as well as the decrease in the possibility of false alarm rates, continue to fall on the negative side of the outcome spectrum, according to the researchers. The hybrid approach has been proposed in this article that includes deep convolutional neural networks, hyperparameter sets utilising adaptive multi-objective CAT algorithms and other image processing techniques. To train the suggested model, it is critical to use previously processed cell images. The suggested model is then used to train an Optimized Convolutional Neural Network (OCNN), which is then used to determine the type of tumour identified in the bone area. The SN-AM datasets were thoroughly examined, and a number of presentation measures, such as accuracy (recall), F1-score, and specificity (specificity), were generated and analysed. When it came to predicting different types of cancer, researchers observed that they had an overall accuracy of 99.45 percent. In terms of recognizing the sorts of cancer cells, the proposed model surpassed all existing learning models. © 2023
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