Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

Luna-Benoso, B.; Martinez-Perales, J. C.; Cortes-Galicia, J.; Flores-Carapia, R.; Silva-Garcia, V. M.

doi:10.3390/computers11010008

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…In particular, the mechanisms are able to recognize the pattern in the dermoscopic images based on the extracted features. When a medical image or signal is analyzed by a CAD system, any anomalies from the typical patterns are detected [36][37][38][39][40]. In general, the underlying premise of the Computer Aided Diagnosis (CAD) system is that nearby is a geometric difference among usual besides unusual images.…”

Section: Fig 3 Multidirectional Representation Systems Using Curvelet...mentioning

confidence: 99%

Multidirectional Analysis of Curvelet Against Skin Cancer

Kolekar,

Razak

et al. 2024

Preprint

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An elevated chance of getting another melanoma is associated with a personal history of the disease. Individuals who have already had a melanoma have a 2–5% probability of getting another one later. Compared to individuals whose initial melanoma was superficial spreading melanoma, those whose first melanoma was lentigo maligns melanoma or nodular melanoma are at a greater peril of emerging a secondary dominant cancer. Melanoma risk is double in those with a special antiquity of squamous cell carcinoma. The likelihood of getting melanoma is doubled if you have a particular times past of basal cell carcinoma. In addition, melanoma risk is higher in persons with actinic keratosis than in those without the condition. An automated technique for classifying melanoma, or skin cancer, is proposed in this work. An image of gathered data is used as the input for the proposed system, and various image handling methods remain smeared to improve the picture's characteristics. The curvelet technique is used to separate benign from malignant skin cancer and to collect relevant data from these pictures so that the classifier may be trained and tested. The basic wrapper curvelet's coefficients are the characteristics that are utilized for classification. Curvelet works well with images that have cartoon edges and aligned textures. In a database of digital photos, the three-layer back-propagation neural network classifier with curvelet has 75.6% recognition accuracy.

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Section: Fig 3 Multidirectional Representation Systems Using Curvelet...mentioning

confidence: 99%

Multidirectional Analysis of Curvelet Against Skin Cancer

Kolekar,

Razak

et al. 2024

Preprint

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“…A skin lesion's prediagnosis using CAD systems is based on clinical standards or structurally related global patterns. Applying measures for accuracy, sensitivity, and specificity, the suggested model, was evaluated against different models using dermoscopic images from the PH2 database [35]. Brain cancer detection and classification is done utilizing distinct medical imaging modalities CT, MRI.…”

Section: -2022mentioning

confidence: 99%

Computer-Aided Design on Image Detection: A Chronology Review

Maziah Mahmud,

Wan Azani Mustafa,

Abd Fatah Wahab

2023

ARASET

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In order to conduct research and diagnose diseases, image processing is essential. Medical professionals frequently segment images for pre- and post-surgery decisions, which are necessary for treatment planning. In the medical research field, segmentation is the core subject of several studies. Computer-aided detection (CAD) is utilised to achieve the highest level of classification accuracy and may be used to identify tissues growing abnormally. For the purpose of finding abnormalities, magnetic resonance imaging (MRI) is an effective approach, but it takes time and requires a fair amount of human resources. This approach, however, was problematic for slicing data related to the interior surfaces of cavity structures, for instance, the human skull. As a result, a ray casting algorithm was used to create a software programme. The most significant problem with segmentation techniques for x-ray images is seed point selection. An object's surface structure is described by a three-dimensional (3D) surface structure graph (SSG) that was created during segmentation. Ultrasound image detection is critical today. The model can be further modified using this CAD software so that it can be reproduced on a rapid prototyping device in the STL file format. The suggested deep learning method is exceptionally effective in accurately detecting faults in each layer, according to experimental data.

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“…These methods attempt to detect global patterns based on three categories: texture, shape, and color [30]. Luna-Benoso et al [31] also used texture descriptors based on statistical measurements. Alizadeh et al [32] converted the image into gray levels and then adopted a local binary pattern and Haralick features as texture features to be extracted from the lesion being tested.…”

Section: Related Studiesmentioning

confidence: 99%

A Decision Support System for Melanoma Diagnosis from Dermoscopic Images

Rízzí

Guaragnella

2022

Applied Sciences

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Innovative technologies in dermatology allow for the early screening of skin cancer, which results in a reduction in the mortality rate and surgical treatments. The diagnosis of melanoma is complex not only because of the number of different lesions but because of the high similarity amongst skin lesions of different nature; hence, human vision and physician experience still play a major role. The adoption of automatic systems would aid clinical assessment and make the diagnosis reproducible by eliminating inter- and intra-observer variabilities. In our paper, we describe a computer-aided system for the early diagnosis of melanoma in dermoscopic images. A soft pre-processing phase is performed so as to avoid the loss of details both in texture, colors, and contours, and color-based image segmentation is later carried out using k-means. Features linked to both geometric properties and color characteristics are used to analyze skin lesions through a support vector machine classifier. The PH2 public database is used for the assessment of the procedure’s sensitivity, specificity, and accuracy. A statistical approach is carried out to establish the impact of image quality on performance. The obtained results show remarkable achievements, so our computer-aided approach should be suitable as a Decision Support System for melanoma detection.

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Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

Cited by 8 publications

References 40 publications

Multidirectional Analysis of Curvelet Against Skin Cancer

Multidirectional Analysis of Curvelet Against Skin Cancer

Computer-Aided Design on Image Detection: A Chronology Review

A Decision Support System for Melanoma Diagnosis from Dermoscopic Images

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