Diagnosing and successfully treating cancer remains to be a formidable challenge. Cancer diagnosis by conventional methods is laborious and highly subjective, reliant on the knowledge and experience of radiologists and pathologists. With the combination of AI and ML technologies, cancer imaging has seen a paradigm change. Medical imaging like CT, MRI, and PET scans may be analyzed using AIML algorithms and deep neural networks for characteristics and patterns that might indicate malignancy. More precise diagnosis and tailored treatment programs are possible with their aid in tumor segmentation and categorization. A type of artificial intelligence that has shown promise in cancer detection is radiomics. One more key approach utilized in AI-powered cancer detection is texture analysis. This technique entails analyzing the spatial organization of pixel intensities in a picture. The genetic elements that contribute to the genesis and progression of cancer are becoming better understood with the development of artificial intelligence systems that can analyze genomic data in addition to medical imaging. This review article delves into the revolutionary effects of AI and ML on cancer imaging, showcasing significant progress, obstacles, and potential solutions. Early detection, diagnosis, and personalized treatment methods are being transformed by these technologies, which are making use of the massive quantities of medical data that are accessible. The result is an improvement in patient outcomes.
Kumar et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: