Breaking the Boundaries of Oncological Diagnosis: A Holistic Framework for Multi-Cancer Identification and Comprehensive Diagnostic Reporting Utilizing Advanced AI and NLP

Abstract

Innovative deep learning models for cancer classification, including VGG-19, DenseNet201, MobileNetV3, ResNet50V2, YOLOv5, and GPT-2, have completely changed the way that doctors diagnose cancer. This study introduces a multi-modal technique for an accurate and speedy cancer diagnosis. With the aid of cutting-edge technology, this system combines object identification (YOLOv5), natural language processing (GPT-2), and picture classification models (VGG-19, DenseNet201, MobileNetV3, ResNet50V2) to provide scientists and medical professionals with a flexible toolkit. It generates thorough reports with tumour images and spots malignant irregularities. Diagnostic accuracy is improved by real-time application, which benefits laboratories by reducing turnaround times and medical practitioners by providing an important decision support tool. Reports with tumour photos enhance the capacity to comprehend results. This study is important because it has the potential to improve cancer detection by using cutting-edge algorithms and models. This research also promises to improve patient care, prediction, and therapy. The practical method presented in this paper ushers in a new age in medical diagnostics by empowering laboratories and doctors.

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