Cancer detection and segmentation using machine learning and deep learning techniques: a review

Rai, Hari Mohan

doi:10.1007/s11042-023-16520-5

Cited by 24 publications

(5 citation statements)

References 133 publications

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“…Rai (2023) [17] conducted a comprehensive analysis of cancer detection and segmentation, utilizing both deep neural network (DNN) and conventional machine learning (CML) methods, covering seven cancer types. The review separately scrutinized the strengths and challenges of DNN and CML classifiers.…”

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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“…Regarding systematic reviews in the area of skin cancer, relevant contributions were found between 2019 and 2023, covering between 19 and 100 articles [17][18][19]. In this scenario, different aims were achieved, such as works presenting a full disease-focused introduction for new research focused on the diagnosis process and generalization of advanced methodologies for detecting multiple types of cancer.…”

Section: Introductionmentioning

confidence: 99%

Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

Hermosilla,

Soto,

Vega

et al. 2024

Diagnostics

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In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.

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“…Following, several studies have highlighted the significant advancements of deep learning algorithms in medical imaging, particularly in the diagnosis and categorization of various diseases, including cancer and skin conditions [9][10][11][12][13][14][15][16]. While many studies focused on diagnosing autoimmune blistering skin diseases using deep neural networks, emphasizing the need for computerized systems to overcome the limitations of current diagnostic methods [9,12], other studies were directed to advanced algorithms for skin lesion segmentation, a critical step in skin cancer diagnosis [10,14,15]. The challenges and recent developments in multiple-lesion recognition, highlighting the complexity of recognizing different lesions simultaneously was explored [11].…”

Section: Introductionmentioning

confidence: 99%

“…The challenges and recent developments in multiple-lesion recognition, highlighting the complexity of recognizing different lesions simultaneously was explored [11]. The use of deep learning in detecting various types of cancer, underlining the role of these technologies in early diagnosis and improved patient outcomes was investigated [13,14]. Segmentation of optical coherence tomography images, a challenging task crucial for diagnosing diseases like glaucoma was explored [15].…”

Section: Introductionmentioning

confidence: 99%

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

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This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

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Cancer detection and segmentation using machine learning and deep learning techniques: a review

Cited by 24 publications

References 133 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

Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

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