Sharma, Arpit KumarNandal, AmitaDhaka, ArvindZhou, LiangAlenezi, FayadhPolat, Kemal2024-05-222024-05-222023Sharma, A. K., Nandal, A., Dhaka, A., Zhou, L., Alhudhaif, A., Alenezi, F., & Polat, K. (2023). Brain tumor classification using the modified ResNet50 model based on transfer learning. Biomedical Signal Processing and Control, 86, 105299.1746-80941746-8108http://dx.doi.org/10.1016/j.bspc.2023.105299https://hdl.handle.net/20.500.12491/12145This work is supported by the Foundation of National Key R & D Program of China [Grant number 2020YFC2008700] ; the National Natural Science Foundation of China [Grant number 82072228] ; the Foundation of Shanghai Municipal Commission of Economy and Informatization [Grant number 202001007] ; the Foundation of the Program of Shanghai Academic/Technology Research Leader under the Science and Technology Innovation Action Plan [Grant number 22XD1401300] ; the Foundation of the Technical Standards Program Under the Science and Technology Innovation Action Plan [Grant number 23DZ2204100] .Brain tumour classification is essential for determining the type and grade and deciding on therapy appropriately. Several diagnostic methods are used in the therapeutic therapy to identify brain tumours. MRI, on the other hand, offers superior picture clarity, which is why specialists depend on it. Furthermore, detecting cancer through the manual division of brain tumours is a time-consuming, exhausting, and difficult job. The handdesigned outlines for planned brain tumour growth methods are present in the majority of the instances. Segmentation is a highly reliable and precise method for assessing therapy prognosis, planning, and outcomes. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) advancements have enabled us to investigate the illness with high precision in a short period of time. Such technologies have produced some remarkable results, particularly in the last twenty years. Such breakthroughs provide doctors with the ability to evaluate the human anatomy using high-resolution sections. The most recent approaches can improve diagnostic precision when examining patients using non-invasive means. This work introduces a brain tumour detection method. The model grows using ResNet50, feature extraction, and augmentation. CNN's pre-trained datasets are used to fine-tune transfer learning. The proposed design utilised elements of the ResNet50 model, removing the final layer and adding four additional layers to meet work conditions. This study uses the improved ResNet50 model to present a novel deep-learning approach based on a transfer learning technique for evaluating brain cancer categorisation accuracy. Performance metrics were used to evaluate the effectiveness of the proposed model, and the results were compared to those obtained using state-of-the-art methods.eninfo:eu-repo/semantics/closedAccessTransfer LearningDeep LearningFeature ExtractionMachine LearningMRI and NeuroimagingMRIArticle10.1016/j.bspc.2023.105299861142-s2.0-85166618367Q1WOS:001050346600001Q1