Automated detection of melanocytes related pigmented skin lesions: A clinical framework

Pathan, Sameena; Prabhu, Kartik; Siddalingaswamy, P. C.

doi:10.1016/j.bspc.2019.02.013

Cited by 31 publications

(20 citation statements)

References 27 publications

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“…These features were fused and classified as benign or malignant using an SVM. Pathana et al [149] proposed a skin lesion CAD system. Garcia-Arroyo et al [150] proposed a skin lesion detection system.…”

Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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Section: Traditional Machine Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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“…Their method was more robust of lesion identification, and its usefulness had been tested using relevant data. A robust ensemble architecture, constructed using dynamic classifier selection techniques, was employed to detect cancer [ 38 ], so that the model can learn more powerful and distinguishing features. A crossnet-based combination of various convolutional networks was suggested as a solution for medical image identification [ 39 ] and proven by extensive testing.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things

Elaziz

Mabrouk

Dahou

et al. 2022

Computational Intelligence and Neuroscience

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Recently, the 6G-enabled Internet of Medical Things (IoMT) has played a key role in the development of functional health systems due to the massive data generated daily from the hospitals. Therefore, the automatic detection and prediction of future risks such as pneumonia and retinal diseases are still under research and study. However, traditional approaches did not yield good results for accurate diagnosis. In this paper, a robust 6G-enabled IoMT framework is proposed for medical image classification with an ensemble learning (EL)-based model. EL is achieved using MobileNet and DenseNet architecture as a feature extraction backbone. In addition, the developed framework uses a modified honey badger algorithm (HBA) based on Levy flight (LFHBA) as a feature selection method that aims to remove the irrelevant features from those extracted features using the EL model. For evaluation of the performance of the proposed framework, the chest X-ray (CXR) dataset and the optical coherence tomography (OCT) dataset were employed. The accuracy of our technique was 87.10% on the CXR dataset and 94.32% on OCT dataset—both very good results. Compared to other current methods, the proposed method is more accurate and efficient than other well-known and popular algorithms.

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“…On the other hand, there are works that inspect the lesion, detecting the specific presence of patterns associated with its structure. These methods attempt to detect global patterns that are mainly divided into three categories, such as texture, shape, and color [22][23][24]. In this work, texture descriptors based on statistical measurements are used.…”

Section: Related Jobsmentioning

confidence: 99%

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

et al. 2022

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Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

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Automated detection of melanocytes related pigmented skin lesions: A clinical framework

Cited by 31 publications

References 27 publications

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

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