Abstract
Artificial intelligence has illustrated drastic changes in radiology and medical imaging techniques which in turn led to tremendous changes in screening patterns. In particular, advancements in these techniques led to the development of computer aided detection (CAD) strategy. These approaches provided highly accurate diagnostic reports which served as a “second-opinion” to the radiologists. However, with significant advancements in artificial intelligence strategy, the diagnostic and classifying capabilities of CAD system are meeting the levels of radiologists and clinicians. Thus, it shifts the CAD system from second opinion approach to a high utility tool. This article reviews the strategies and algorithms developed using artificial intelligence for the foremost cancer diagnosis and classification which overcomes the challenges in the traditional method. In addition, the possible direction of AI in medical aspects is also discussed in this study.
Highlights
Cancer being one of the non-communicable diseases is ranked at the foremost for being the blockade of survival rate among the global population
The evolution of artificial intelligence (AI) in cancer imaging described in this study provides the significance of AI in cancer diagnosis and treatment
In order to manifest the potential of artificial intelligence in cancer imaging, the following case studies were described in this paper:
Summary
Cancer being one of the non-communicable diseases is ranked at the foremost for being the blockade of survival rate among the global population. Due to the rise of cancer incidence rate, diagnosing of disease using conventional tools at early stage had become difficult. These traditional methods experienced diagnostic errors including missed, wrong and delayed cases [2]. The perplexity of cancer includes early detection, accuracy, tumor evolution, metastasis pattern, recurrence, tumor aggressiveness and determination of tumor margins [3]. To overcome the limitations mentioned above and to diagnose cancer at the earliest, advancement in artificial intelligence (AI) had been raised for quantifying the imaging data. Despite the need for a large quantity of data for training, deep learning had illustrated relative
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