A Proficient Approach to Detect Osteosarcoma Through Deep Learning

• Published in: 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22) pp. 1 - 6
• Main Authors: Ahammad, Mejbah, Abedin, Mohammad Joynul, Khan, Md. Asiqur Rahman, Alim, Md. Abdul, Rony, Mohammad Abu Tareq, Alam, K.M. Rashedul, Reza, D. S. A. Aashiqur, Uddin, Iktear
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29-04-2022
• Subjects: Annotation; Augmentation; Bones; Convolutional neural network; Deep learning; fastai; Grad-CAM; Information processing; Legged locomotion; Neural networks; Organizations; Osteosarcoma; Segmentation
• DOI: 10.1109/ICETET-SIP-2254415.2022.9791502
• EISSN: 2157-0485

Osteosarcoma is a life-threatening bone cancer that usually attacks young adults and children, independent of age. It habitually starts in quick-growing bone areas close to the ends of the arm or leg bones, such as the distal femur, proximal tibia, and proximal humerus. However, it can still be revealed in any bone, including the pelvis, jaw, and shoulder. The starting and the preeminent conclusion of any cancer are to identify the tumor as before long as conceivable, and it's moreover pertinent for Osteosarcoma. Osteosarcoma has a few arrange in its life cycle. The need of categorizing cancer patients into tall or short risk categories has prompted several research organizations in the biomedical and bioinformatics fields to consider using Profound Learning (Deep Learning) methodologies. Fast.ai, a Deep Learning Framework for enhancing the efficiency and accu-racy of osteosarcoma tumor categorization into tumor classes, is presented in this study (tumor vs non-tumor). At the conclusion of the study, we found that employing neural networks may provide excellent precision and capability in osteosarcoma classification and model comparison.