DPV: a taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection

Shah, Bhagyashree; Alsadoon, Abeer; Prasad, P. W. C.; Al‐Naymat, Ghazi; Beg, Azam

doi:10.1007/s11042-021-10769-4

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…However, limitations included a combined treatment of machine learning and deep learning without a separate analysis and the absence of a comparative exploration between the two methodologies. Shah et al (2021) [12] proposed predictive systems for various cancer types but had limitations. It used a data, prediction technique, and view (DPV) framework to assess cancer detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Rai,

Yoo

2023

Mathematics

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Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored cancer classification using machine learning and deep learning techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake a comprehensive review of colorectal and gastric cancer detection specifically employing traditional ML classifiers. This review emphasizes the mathematical underpinnings of cancer detection, encompassing preprocessing techniques, feature extraction, machine learning classifiers, and performance assessment metrics. We provide mathematical formulations for these key components. Our analysis is limited to peer-reviewed articles published between 2017 and 2023, exclusively considering medical imaging datasets. Benchmark and publicly available imaging datasets for colorectal and gastric cancers are presented. This review synthesizes findings from 20 articles on colorectal cancer and 16 on gastric cancer, culminating in a total of 36 research articles. A significant focus is placed on mathematical formulations for commonly used preprocessing techniques, features, ML classifiers, and assessment metrics. Crucially, we introduce our optimized methodology for the detection of both colorectal and gastric cancers. Our performance metrics analysis reveals remarkable results: 100% accuracy in both cancer types, but with the lowest sensitivity recorded at 43.1% for gastric cancer.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Rai,

Yoo

2023

Mathematics

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“…Deep learning (DL) is a type of machine learning method, and the learning process takes place on an artificial neural network model with more intermediate layers. Deep learning methods can classify large amounts of data with higher accuracy to provide analytical results based on the parameters and objectives of a particular framework[15]. Deep learning is mainly used in image segmentation, disease prediction, and recommendation systems such as convolutional neural networks, autoencoders, and restricted Boltzmann machines…”

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confidence: 99%

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Aksakalli

Kaçdioğlu

Hanay

2021

Balkan Journal of Electrical and Computer Engineering

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Today, kidney stone detection is performed manually by humans on medical images. This process is timeconsuming and subjective as it depends on the physician. This study aims to classify healthy or patient individuals according to the status of kidney stones from medical images using various machine learning methods and Convolutional Neural Network (CNN). We evaluated various machine learning methods such as Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (kNN), Naive Bayes (BernoulliNB), and deep neural networks using CNN. According to the experiments, the Decision Tree Classifier (DT) has the best classification result. This method has the highest F1 score rate with a success rate of 85.3% using the S+U sampling method. The experimental results show that the Decision Tree Classifier (DT) is a feasible method for distinguishing the kidney x-ray images.

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DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

Alsadoon

Al‐Naymat

Osman

et al. 2023

Multimed Tools Appl

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DPV: a taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection

Cited by 7 publications

References 35 publications

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

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