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Hermosilla P, Soto R, Vega E, Suazo C, Ponce J. Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review. Diagnostics (Basel) 2024; 14:454. [PMID: 38396492 PMCID: PMC10888121 DOI: 10.3390/diagnostics14040454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/07/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
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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Affiliation(s)
- Pamela Hermosilla
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile (E.V.); (C.S.); (J.P.)
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Mirikharaji Z, Abhishek K, Bissoto A, Barata C, Avila S, Valle E, Celebi ME, Hamarneh G. A survey on deep learning for skin lesion segmentation. Med Image Anal 2023; 88:102863. [PMID: 37343323 DOI: 10.1016/j.media.2023.102863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 02/01/2023] [Accepted: 05/31/2023] [Indexed: 06/23/2023]
Abstract
Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online3.
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Affiliation(s)
- Zahra Mirikharaji
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada
| | - Kumar Abhishek
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada
| | - Alceu Bissoto
- RECOD.ai Lab, Institute of Computing, University of Campinas, Av. Albert Einstein 1251, Campinas 13083-852, Brazil
| | - Catarina Barata
- Institute for Systems and Robotics, Instituto Superior Técnico, Avenida Rovisco Pais, Lisbon 1049-001, Portugal
| | - Sandra Avila
- RECOD.ai Lab, Institute of Computing, University of Campinas, Av. Albert Einstein 1251, Campinas 13083-852, Brazil
| | - Eduardo Valle
- RECOD.ai Lab, School of Electrical and Computing Engineering, University of Campinas, Av. Albert Einstein 400, Campinas 13083-952, Brazil
| | - M Emre Celebi
- Department of Computer Science and Engineering, University of Central Arkansas, 201 Donaghey Ave., Conway, AR 72035, USA.
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada.
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Hasan MK, Ahamad MA, Yap CH, Yang G. A survey, review, and future trends of skin lesion segmentation and classification. Comput Biol Med 2023; 155:106624. [PMID: 36774890 DOI: 10.1016/j.compbiomed.2023.106624] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/04/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023]
Abstract
The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.
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Affiliation(s)
- Md Kamrul Hasan
- Department of Bioengineering, Imperial College London, UK; Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
| | - Md Asif Ahamad
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.
| | - Choon Hwai Yap
- Department of Bioengineering, Imperial College London, UK.
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, UK; Cardiovascular Research Centre, Royal Brompton Hospital, UK.
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Yue G, Han W, Li S, Zhou T, Lv J, Wang T. Automated polyp segmentation in colonoscopy images via deep network with lesion-aware feature selection and refinement. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Lucieri A, Bajwa MN, Braun SA, Malik MI, Dengel A, Ahmed S. ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106620. [PMID: 35033756 DOI: 10.1016/j.cmpb.2022.106620] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/01/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES One principal impediment in the successful deployment of Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday clinical workflows is their lack of transparent decision-making. Although commonly used eXplainable AI (XAI) methods provide insights into these largely opaque algorithms, such explanations are usually convoluted and not readily comprehensible. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is ambiguous in itself. This work presents ExAID (Explainable AI for Dermatology), a novel XAI framework for biomedical image analysis that provides multi-modal concept-based explanations, consisting of easy-to-understand textual explanations and visual maps, to justify the predictions. METHODS Our framework relies on Concept Activation Vectors to map human-understandable concepts to those learned by an arbitrary Deep Learning (DL) based algorithm, and Concept Localisation Maps to highlight those concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All decision-related information is presented in a diagnostic interface for use in clinical routines. Moreover, the framework includes an educational mode providing dataset-level explanation statistics as well as tools for data and model exploration to aid medical research and education processes. RESULTS Through rigorous quantitative and qualitative evaluation of our framework on a range of publicly available dermoscopic image datasets, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong disease predictions. We demonstrate that concept detectors for the explanation of pre-trained networks reach accuracies of up to 81.46%, which is comparable to supervised networks trained end-to-end. CONCLUSIONS We present a new end-to-end framework for the multi-modal explanation of DL-based biomedical image analysis in Melanoma classification and evaluate its utility on an array of datasets. Since perspicuous explanation is one of the cornerstones of any CAD system, we believe that ExAID will accelerate the transition from AI research to practice by providing dermatologists and researchers with an effective tool that they can both understand and trust. ExAID can also serve as the basis for similar applications in other biomedical fields.
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Affiliation(s)
- Adriano Lucieri
- German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 122, 67663 Kaiserslautern, Germany; Technical University Kaiserslautern, Erwin-Schrödinger-Straße 52, 67663 Kaiserslautern, Germany.
| | - Muhammad Naseer Bajwa
- German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 122, 67663 Kaiserslautern, Germany; Technical University Kaiserslautern, Erwin-Schrödinger-Straße 52, 67663 Kaiserslautern, Germany.
| | - Stephan Alexander Braun
- University Hospital Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany; University Hospital of Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany.
| | - Muhammad Imran Malik
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan; Deep Learning Laboratory, National Center of Artificial Intelligence, Islamabad, Pakistan.
| | - Andreas Dengel
- German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 122, 67663 Kaiserslautern, Germany; Technical University Kaiserslautern, Erwin-Schrödinger-Straße 52, 67663 Kaiserslautern, Germany.
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 122, 67663 Kaiserslautern, Germany.
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Hasan MK, Alam MA, Dahal L, Roy S, Wahid SR, Elahi MTE, Martí R, Khanal B. Challenges of deep learning methods for COVID-19 detection using public datasets. INFORMATICS IN MEDICINE UNLOCKED 2022; 30:100945. [PMID: 35434261 PMCID: PMC9005223 DOI: 10.1016/j.imu.2022.100945] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 04/03/2022] [Accepted: 04/04/2022] [Indexed: 02/07/2023] Open
Abstract
Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train-test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train-test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train-test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The dataset should have an independent test set; for a single machine or hospital source, have a more balanced set of images for all the prediction classes; and have a balanced dataset from several hospitals and demography. Our source codes and model are publicly available for the research community for further improvements.
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Affiliation(s)
- Md. Kamrul Hasan
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh,Correspondence to: Department of EEE, KUET, Khulna 9203, Bangladesh
| | - Md. Ashraful Alam
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Lavsen Dahal
- Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Nepal
| | - Shidhartho Roy
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Sifat Redwan Wahid
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Md. Toufick E. Elahi
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Robert Martí
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Bishesh Khanal
- Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Nepal
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Hasan MK, Elahi MTE, Alam MA, Jawad MT, Martí R. DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2021.100819] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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