A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics

Mohan, Hari; Yoo, Joon

doi:10.1007/s00432-023-05216-w

Cited by 16 publications

(3 citation statements)

References 169 publications

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“…However, limitations included the study's focus on four cancer types, the lack of a dataset analysis, and reliance on a single assessment metric. Rai and Yoo (2023) [20] enhanced cancer diagnostics by classifying four cancer types with computational machine learning (CML) and deep neural network (DNN) methods. The study reviewed 130 pieces of literature, outlined benchmark datasets and features, and presented a comparative analysis of CML and DNN models.…”

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

Self Cite

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“…In other words, we can say that advanced technology like deep learning is now being used to detect various types of cancer, including brain tumors, breast cancer, lung cancer, esophageal cancer, and skin lesions on the feet. Doctors use imaging methods like dermoscopy [6], CT scans, HRCT scans, and MRI to diagnose cancer and gather information about skin cancer in patients worldwide [7]. To make this technology work, fast internet, powerful computers, and reliable online storage are needed to collect and share skin cancer data.…”

Section: Introductionmentioning

confidence: 99%

“…However, this process takes a lot of time, which can cause patients' conditions to worsen [8]. Data scientists mostly use the HAM1000 dataset to training and testing machine learning models for automatic skin lesion classification/prediction, helping in the early detection of skin diseases such as melanoma [6] [1]. Melanoma is one of the types of skin cancer, and used to early detection greatly improves the chances of successful treatment.…”

Section: Introductionmentioning

confidence: 99%

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

Khan,

S.,

Gangodkar

2024

Preprint

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Skin diseases, disorders, and deficiencies are fundamental issues in the human body. Over the last few decades, they have become widely prevalent, often leading to cancer. This spectrum of ailments ranges from benign conditions to potentially life-threatening cancers, posing significant challenges to global health. Accurate classification of these diseases and timely prediction of malignant transformations are crucial for effective diagnosis and treatment planning. In our research paper, we propose an innovative approach that levitation multimodal data integration to enhance the precision of skin disease classification and improve the prediction of cancerous transformations. The study utilizes the HAM_10000 Metadata, a dataset comprising diverse skin lesions, along with 100 high-resolution images of Squamous Cell Carcinoma (SCC). Pre-trained models, specifically multi-model CNNs, are employed to extract intricate patterns and features. By amalgamating information from these heterogeneous sources and implementing advanced machine learning techniques, the results highlight the effectiveness of this integrative methodology. This approach can revolutionize the fields of dermatology and oncology by providing patients with skin diseases accurate, efficient, and early diagnoses of skin disease. Through this method, it was discovered that SCC images might hold a higher priority for skin cancer identification, achieving an accuracy rate of 92% on the original dataset.

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A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction

Rai,

Yoo,

Razaque

2024

Med Biol Eng Comput

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A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics

Cited by 16 publications

References 169 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

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction

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