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Machine Learning and Computer Vision Based Methods for Cancer Classification: A Systematic Review

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Abstract

Cancer remains a substantial worldwide health issue that requires careful and exact classification to plan treatment in its early stages. Classical methods of cancer diagnosis involve lab-based testing using biopsy, and imaging tests. Modern technologies may contribute effectively to speed up the diagnosis of cancer. Machine learning-based algorithms have been more prominent in cancer classification in recent years. These algorithms hold great promise in interpreting complex datasets and applying the learned knowledge to categorize unseen samples for cancer classification. In addition, many computer vision-based algorithms play a vital role in image pre-processing, segmentation, and feature extraction. This review article discusses nine major cancer types: carcinoma, sarcoma, neuroendocrine tumor, melanoma, lymphoma, germ cell tumor, leukemia, brain tumor, and multiple myeloma. We conducted a detailed survey of recent literature. We focused on systems that utilize clinical imaging modalities as input and preprocessing, segmentation, and feature extraction as intermediate stages with machine learning classifier as their concluding stage. We have examined the works that classify cancer as mentioned above types using machine learning algorithms. We have analyzed six prominent machine learning-based algorithms: Support vector machines, decision trees, random forest, Naïve Bayes, logistic regression, and K-nearest neighbors. This work also gives insights into various imaging modalities, such as Computed Tomography scan, histopathological images, dermoscopic images, and their utility in diagnosing cancer. In addition, the paper discusses the performance measures used for evaluating the efficiency of machine learning-based models, including accuracy, sensitivity, specificity, F1-score. We have reviewed various pre-processing and segmentation techniques suitable for clinical image-based cancer classification. This survey also discusses some significant challenges researchers face during cancer classification studies. The main objective of this systematic review is to provide researchers and medical experts with extensive knowledge of the present status of cancer classification with the aid of computer vision and machine learning-based systems. We intend to provide a foundation for enhanced cancer detection and therapy precision using these techniques. This effort eventually contributes to the progression of the field of cancer and the enhancement of patient predictions. In addition, we have recognized a few possible directions for research in this domain.

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Abbreviations

ABCDE:

Asymmetry, irregularity of border, color of Dermoscopy image, diameter, and evolution

ADNI:

Alzheimer’s Disease Neuroimaging Initiative

AI:

Artificial Intelligence

ALL:

Acute Lymphoblastic Leukemia

AS:

Adaptive Snake

AT:

Adaptive Thresholding

AUC:

Area Under Curve

BSI:

Blood Smear Images

CBC:

Complete Blood Count

CCH:

Conventional Color Histogram

C-LS:

Level Set Method of Chan

CNN:

Convolutional Neural Network

CP:

Cholangiocarcinoma Patients

CT:

Computed Tomography

CV:

Computer Vision

DNN:

Deep Neural Network

DT:

Decision Tree

DWT:

Discrete Wavelet Transform

EM:

Expectation–Maximization

EM-LS:

Expectation–Maximization Level Set

FBSM:

Fuzzy-Based Split-And-Merge Algorithm

FNAB:

Fine-Needle Aspiration Biopsy

GA:

Genetic Algorithm

GLCM:

Gray Level Co-Occurrence Matrix

GLDM:

Gray Level Dependence Matrix

GLSZM:

Gray-Level Size Zone Matrix

GNG:

Growing Neural Gas

GVF:

Gradient Vector Flow

GWT:

Gabor Wavelet Transform

HL:

Hodgkin Lymphoma

HOG:

Histogram of Oriented Gradient

HSV:

Hue Saturation Value

K-NN:

K-Nearest Neighbor

KSVM:

Kernal support vector machine

LBG:

Linde Buzo Gray

LBP:

Local Binary Pattern

LDA:

Linear Discriminant Analysis

LR:

Logistic Regression

LTE:

Laws’ Texture Energy

LUNA:

LUng Nodule Analysis

MAP:

Maximum A Posterior

MAS:

Multi Atlas Segmentation

MI:

Moments Invariant

MIAS:

Mammographic Image Analysis Society

ML:

Machine Learning

MM:

Multiple Myeloma

MRF:

Markov Random Fields

MRI:

Magnetic Resonance Imaging

MRMR:

Minimum Redundancy Maximum Relevance

NB:

Naïve Bayes

NCI:

National Cancer Institute

NGTDM:

Neighborhood Grey-Tone Difference Matrix

NHL:

Non-Hodgkin Lymphoma

OASIS:

Open Access Series of Imaging Studies

OBNLM:

Optimized Bayesian Non-Local Means

PCA:

Principal Component Analysis

PDF:

Probability Density Function

PET:

Positron Emission Tomography

PSNR:

Peak Signal-to-Noise Ratio

PSO:

Particle Swarm Optimization

RBC:

Red Blood Cells

RF:

Random Forrest

ROI:

Region Of Interest

SGLDM:

Spatial gray-level dependence matrix

SIFT:

Scale Invariance Feature Transformation

SMOTE:

Synthetic Minority Oversampling Technique

STS:

Soft Tissue Sarcoma

SURF:

Speeded Up Robust Features

SVM:

Support Vector Machine

TBC:

Tumor Border Clarity

TGCA:

The Cancer Genome Atlas

US:

Ultra Sound

UV:

Ultra Violet

WBC:

White Blood Cells

WBCD:

Wisconsin Breast Cancer Diagnostic

WDO:

Wind-Driven Optimization

WHO:

World Health Organization

WNNM:

Weighted Nuclear Norm Minimization

WSM:

Wavelet Sub-Band Coefficient Mixing

µCT:

Micro Computed Tomography

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Mukadam, S.B., Patil, H.Y. Machine Learning and Computer Vision Based Methods for Cancer Classification: A Systematic Review. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10065-y

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