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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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Derekas P, Spyridonos P, Likas A, Zampeta A, Gaitanis G, Bassukas I. The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers (Basel) 2023; 15:4861. [PMID: 37835555 PMCID: PMC10571759 DOI: 10.3390/cancers15194861] [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: 08/18/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023] Open
Abstract
AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of the AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (i.e., AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to the low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts of perilesional skin. In total, 16,488 translation-augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-Net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving upon both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.
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Affiliation(s)
- Panagiotis Derekas
- Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece; (P.D.); (A.L.)
| | - Panagiota Spyridonos
- Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Aristidis Likas
- Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece; (P.D.); (A.L.)
| | - Athanasia Zampeta
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
| | - Georgios Gaitanis
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
| | - Ioannis Bassukas
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (A.Z.); (G.G.); (I.B.)
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Tamoor M, Naseer A, Khan A, Zafar K. Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods. Diagnostics (Basel) 2023; 13:2684. [PMID: 37627943 PMCID: PMC10453628 DOI: 10.3390/diagnostics13162684] [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: 05/30/2023] [Revised: 07/26/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
In recent times, there has been a huge increase in the average number of cases of skin cancer per year, which sometimes become life threatening for humans. Early detection of various skin diseases through automated detection techniques plays a crucial role. However, the presence of numerous artefacts makes this task challenging. Dermoscopic images exhibit various variations, including hair artefacts, markers, and ill-defined boundaries. These artefacts make automatic analysis of skin lesion quite a difficult task. To address these issues, it is essential to have an accurate and efficient automated method which will delineate a skin lesion from the rest of the image. Unfortunately, due to the presence of several types of skin artefacts, there is no such thresholding method that can provide a sufficient segmentation result for every type of skin lesion. To overcome this limitation, an ensemble-based method is proposed that selects the optimal thresholding based on an objective function. A group of state-of-the-art different thresholding methods such as Otsu, Kapur, Harris hawk, and grey level are used. The proposed method obtained superior results (dice score = 0.89 with p-value ≤ 0.05) as compared to other state-of-the-art methods (Otsu = 0.79, Kapur = 0.80, Harris hawk = 0.60, grey level = 0.69, active contour model = 0.72). The experiments conducted in this study utilize the ISIC 2016 dataset, which is publicly available and specifically designed for skin-related research. Accurate segmentation will help in the early detection of many skin diseases.
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Affiliation(s)
- Maria Tamoor
- Department of Computer Science, Forman Christian College, Lahore 54600, Pakistan; (M.T.); (A.K.)
| | - Asma Naseer
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan;
| | - Ayesha Khan
- Department of Computer Science, Forman Christian College, Lahore 54600, Pakistan; (M.T.); (A.K.)
| | - Kashif Zafar
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore 54770, Pakistan;
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Spyridonos P, Gaitanis G, Likas A, Seretis K, Moschovos V, Feldmeyer L, Heidemeyer K, Zampeta A, Bassukas ID. Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma. Cancers (Basel) 2023; 15:3539. [PMID: 37509205 PMCID: PMC10377636 DOI: 10.3390/cancers15143539] [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: 06/10/2023] [Revised: 07/04/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Efficient management of basal cell carcinomas (BCC) requires reliable assessments of both tumors and post-treatment scars. We aimed to estimate image similarity metrics that account for BCC's perceptual color and texture deviation from perilesional skin. In total, 176 clinical photographs of BCC were assessed by six physicians using a visual deviation scale. Internal consistency and inter-rater agreement were estimated using Cronbach's α, weighted Gwet's AC2, and quadratic Cohen's kappa. The mean visual scores were used to validate a range of similarity metrics employing different color spaces, distances, and image embeddings from a pre-trained VGG16 neural network. The calculated similarities were transformed into discrete values using ordinal logistic regression models. The Bray-Curtis distance in the YIQ color model and rectified embeddings from the 'fc6' layer minimized the mean squared error and demonstrated strong performance in representing perceptual similarities. Box plot analysis and the Wilcoxon rank-sum test were used to visualize and compare the levels of agreement, conducted on a random validation round between the two groups: 'Human-System' and 'Human-Human.' The proposed metrics were comparable in terms of internal consistency and agreement with human raters. The findings suggest that the proposed metrics offer a robust and cost-effective approach to monitoring BCC treatment outcomes in clinical settings.
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Affiliation(s)
- Panagiota Spyridonos
- Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Georgios Gaitanis
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Aristidis Likas
- Department of Computer Science & Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece
| | - Konstantinos Seretis
- Department of Plastic Surgery and Burns, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Vasileios Moschovos
- Department of Plastic Surgery and Burns, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Laurence Feldmeyer
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Kristine Heidemeyer
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Athanasia Zampeta
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
| | - Ioannis D Bassukas
- Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
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Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification. Cancers (Basel) 2022; 14:cancers14225716. [PMID: 36428808 PMCID: PMC9688253 DOI: 10.3390/cancers14225716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022] Open
Abstract
This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS.
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