An improved deep convolutionary neural network for bone marrow cancer detection using image processing

• Published in: Informatics in medicine unlocked Vol. 38; p. 101233
• Main Authors: Ramasamy, Manjula Devi, Dhanaraj, Rajesh Kumar, Pani, Subhendu Kumar, Das, Rashmi Prava, Movassagh, Ali Akbar, Gheisari, Mehdi, Liu, Yang, Porkar, P., Banu, Sabitha
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2023; Elsevier
• Subjects: Bone marrow; CAT Algorithm; Leukaemia; Multiple myeloma; Optimized convolutional neural network; Bone marrow; Optimized convolutional neural network; CAT Algorithm; Multiple myeloma; Leukaemia
• ISSN: 2352-9148; 2352-9148
• DOI: 10.1016/j.imu.2023.101233

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.