Deep Learning in Cancer Identification
Intekhab Alam, Shoukath Ali K, Uzma Noor Shah · 2024
Commencing with an exposition on the fundamental tenets underpinning cancer diagnosis, elucidating the sequential phases integral to the diagnostic procedure, and delving into the conventional classification methodologies wielded by medical professionals, this abstract will embark on a comprehensive exploration. Within these pages, readers will embark on a historical odyssey through the annals of cancer classification methodologies, providing insights into their evolution over time. While these established techniques serve as stalwarts in the realm of cancer diagnosis, their effectiveness is tinged with limitations. Furthermore, we shall furnish a succinct compendium of pivotal evaluation metrics, tailored to cater to diverse audiences, encompassing the receiver operating characteristic curve (ROC curve), the area under the ROC curve (AUC), and the F1 score.In light of the deficiencies associated with earlier techniques, there arises an escalating demand for more sophisticated and discerning approaches to cancer diagnosis. Enter the realm of artificial intelligence, poised to chart a transformative trajectory in the landscape of diagnostic tools. In particular, the potential resonance of deep neural networks beckons as an avenue for intelligent image analysis, promising unprecedented strides in diagnostic precision. Our discourse shall unravel the intricacies of pre-processing, image segmentation, and post-processing techniques within the foundational framework of machine learning applied to medical imaging, illuminating the path towards enhanced diagnostic capabilities.