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Review
. 2023 Apr 27;13(9):1563.
doi: 10.3390/diagnostics13091563.

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials

Somit Jain  1 , Dharmik Naicker  1 , Ritu Raj  1 , Vedanshu Patel  1 , Yuh-Chung Hu  2 , Kathiravan Srinivasan  1 , Chun-Ping Jen  3   4
Affiliations
Review

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials

Somit Jain et al. Diagnostics (Basel). .

Abstract

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.

Keywords: artificial intelligence; cancer diagnosis; computer-aided diagnostics; deep learning; machine learning; man-machine systems; smart phone applications.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram for the records identified by searching the database using PRISMA-ScR method.
Figure 2
Figure 2
An illustration of the structure of this review.
Figure 3
Figure 3
Machine learning and Deep Learning models used in various cancer diagnoses.
Figure 4
Figure 4
The progression of Naïve Bayes in cancer diagnosis.
Figure 5
Figure 5
Decision Tree Model.
Figure 6
Figure 6
A deep neural network with multiple layers.
Figure 7
Figure 7
Open challenges in the diagnosis of various cancers.
Figure 8
Figure 8
Future research directions for the diagnosis of various cancers.
See this image and copyright information in PMC

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