Despite recent advances in several imaging modalities, the poor fate of pancreatic tumours has remained a worry in recent decades. The inability to detect pancreatic tumours in their early stages is often due to the organ's small size, its attenuation being similar to that of normal-sized pancreas, or the fact that it is hidden during CT scans. This work presents a systematic approach to monitoring, forecasting and classifying pancreatic tumours. By combining the promising aspects of algorithms influenced by nature with Deep Neural Network (DNN) technology, the proposed model strikes the perfect balance between the two methods. The proposed model uses BAT-ML image segmentation on a CT dataset to look for pancreatic tumours in medical images obtained from CT scans.In terms of sensitivity, specificity, accuracy and F1 score, the suggested model is compared to other current models such as IDLDMS, weighted KLM and Kernel-ELM. Achieving a classification accuracy of 99.61%, the proposed model demonstrates superior performance compared to these existing approaches.
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