AI Integrated application to identify lung cancers and colon cancers in cancer genome atlas (TCGA)

Abstract

"Colon cancer and lung cancer represent significant public health challenges globally, being leading causes of cancer-related mortality. Early detection plays a pivotal role in mitigating these burdens by facilitating timely intervention and treatment. Leveraging advancements in machine learning and artificial intelligence (AI), this abstract delineates recent strides and challenges encountered in employing these technologies for the detection of colon cancer and lung cancer. In the domain of colon cancer detection, machine learning algorithms are instrumental in scrutinizing medical imaging data, notably colonoscopy and computed tomography (CT) scans, to discern suspicious lesions and polyps indicative of malignancy. Sophisticated deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are deployed for feature extraction and classification of colorectal anomalies. Moreover, AI-driven decision support systems aid clinicians in interpreting imaging results, augmenting diagnostic accuracy and informed decision-making. Similarly, in the realm of lung cancer detection, machine learning methodologies leverage diverse imaging modalities such as chest X-rays and CT scans to identify nodules and lesions indicative of malignant growth. Advanced deep learning frameworks, including CNNs and generative adversarial networks (GANs), are harnessed for nodule segmentation, classification, and risk stratification. Additionally, AI-driven radiomics techniques extract quantitative imaging features to prognosticate patient outcomes and treatment response. Despite notable advancements, challenges persist in the domain of colon cancer and lung cancer detection utilizing machine learning and AI. These encompass the exigency for large and diverse datasets, elucidating model interpretability, navigating regulatory compliance, and seamless integration into clinical workflows. Future research endeavors are poised to address these challenges and further refine the accuracy, efficacy, and accessibility of AI-powered cancer detection systems. In summation, the intersection of machine learning and artificial intelligence holds profound promise in revolutionizing early cancer detection, particularly in the contexts of colon cancer and lung cancer. By harnessing cutting-edge algorithms and imaging techniques, these technologies stand to substantially enhance patient outcomes, diminish healthcare burdens, and ultimately preserve lives. Collaborative efforts among researchers, clinicians, and industry stakeholders are imperative in realizing the transformative potential of AI in cancer detection and management. "