Improved deep learning techniques for better cancer diagnosis

Abstract

Over the past several decades, Computer-Aided Diagnosis (CAD) for diagnosis of medical images has prospered due to the advancements in the digital world, advancements in software, hardware and precise and fine-tune images acquired from sensors. With the advancement in the field of medical and applications of Artificial Intelligence scaling to the height of improvement, modern state-of-the-art applications of Deep Learning for better cancer diagnosis have been incepted in recent years. CAD and computerized algorithms and solutions in diagnosing cancer obtained from different modalities, i.e., MRI, CT scans, OCT and so on plays an immense impact on disease diagnosis. Learning model based on transfer mechanisms that stored knowledge for one aspect and using it for another aspect with Deep Convolutional Neural Network paved the way for automatic diagnosis. Recently, improved deep learning algorithm has resulted in great success resulting in robust image characteristics, involving higher dimensions. Analysis of bi-cubic interpolation preprocessing technique paves way for robust obtaining of a region of interest. For an inflexible object with a higher amount of dissimilarity, a comprehensive form for detecting the region of interest and determination of actual positioning may not be robust. Robust perception and localization schemes are analyzed. By integrating Deep Learning with Neighborhood Position Search unseen cases are said to be identified and segmented accordingly via Maximum Likelihood decision rule, forming robust segmentation. The favorable result of an a better cancer diagnosis is indeed contingent on the cancer diagnosis however, an anticipating prediction should consider certain factors more than a straight forward diagnostic decision. Besides the application of different medical data analyses and image processing techniques used in the study of cancer diagnosis deeper insights of the relevant solutions in the light of higher collections of deep learning techniques are found to be vital. Hence, certain factors to be analyzed are the forecasting of risk involved, forecasting of cancer frequency and the forecasting of cancer survival. These factors are analyzed according to the diagnosis criterion, sensitivity, specificity, and accuracy.