MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641102469 A) filed by Saveetha Engineering College on August 25, 2026, for Revolutionizing Cancer Diagnostics: Early Detection Of Colon And Pancreatic Cancer Using Deep Learning And Computer Vision.
Inventor includes Yashini P.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The early identification of cancer leads to better treatment success rates • along with increased survival chances specifically in cases of aggressive forms like colon and pancreatic cancer because patients typically receive their diagnosisfter these c.ancers have progressed to later stages. The modern integration of deep learning techniques with computer vision technology creates opportunities for automatic rapid diagnostic assessments with high precision concerning medical images. The research design introduces a new deep learning system that joins Convolutional Neural Networks (CNN) with Vision Transformers (ViT) for detecting early colon and pancreatic cancer cases. The research method links the ResNet50 feature extraction capabilities with the interpretive strengths of Vision Transformers to produce diagnostic improvements. Data collection focused on histopathological images together with CT scans and MRI images from public repositories underwent preprocessing then data augmentation to ensure the results remained robust. Performance evaluation of the model utilized cross-validation approaches which yielded results for accuracy in addition to precision, recall and AUC-ROC metrics. The proposed system accomplished detection accuracy at 96.2% for colon cancer diagnosis while reaching 94.5% accuracy in pancreatic cancer detection which proved superior to multiple baseline systems. Model interpretation tools such as Grad-CAM revealed that the model properly identified cancerous areas. The Patent describes how models that unite transformer-based structures with CNNs enable healthcare professionals to capture information from medical images at both global and local scales The developed model indicates strong potential to serve as a dependable diagnostic tool for physicians working in oncology and radiology departments so they can deliver better clinical results to patients. The 77pron*ing-study fin,dingslweatp new research opportupities_which focus on uniting clinical and genomic information and implementing the model directly into real-time clinical settings. The research enhances the current trends in Artificial Intelligence applications for healthcare improvement by developing advanced diagnostic systems which detect cancer in early stages.
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