Early stage detection and classification of melanoma

Bhati, Prashant; Singhal, Manish

doi:10.1109/ccintels.2015.7437904

Cited by 21 publications

(4 citation statements)

References 5 publications

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“…Therefore, the early detection of melanoma is essential. We want to be able to detect melanoma within the earlier stages [3]. Once melanoma is detected, we want to reconstruct the lesion into a 3D holographic projection to examine the skin lesion's depth.…”

Section: Introductionmentioning

confidence: 99%

The Depth Estimation and Visualization of Dermatological Lesions: A Novel Methodology (Preprint)

Parekh,

Oyeleke

2024

Preprint

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BACKGROUND Thus far, considerable research has been focused on classifying a lesion as benign or malignant. OBJECTIVE To propose a novel methodology for the depth estimation and visualization of skin lesions. METHODS We start by doing the same using a CNN, followed by using Explainable AI (XAI) to localize the image features responsible for the CNN output. We apply computer graphics for depth estimation and developing the 3D structure of the lesion. Our novel method, called the red spot analysis, measures the degree of infection based on which a conical hologram is constructed. Physicians can study this hologram via Mixed Reality headsets. RESULTS The neural model achieves an accuracy of 85.61%. We successfully obtained 3D representations of lesion depth using the method stated above. CONCLUSIONS When we map the CNN outputs (benign or malignant) to the corresponding hologram, we observe that a malignant lesion has a higher concentration of red spots (infection) in the upper and deeper portions of the skin. CLINICALTRIAL We do not perform RCT for this study.

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Section: Introductionmentioning

confidence: 99%

The Depth Estimation and Visualization of Dermatological Lesions: A Novel Methodology (Preprint)

Parekh,

Oyeleke

2024

Preprint

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“…Several studies related to melanoma detection have previously been carried out. Bhati and Singhal [4] classify skin lesions into malignant (cancerous) and benign (non-cancerous) based on Otsu segmentation methods. Otsu segmentation is a thresholding method for separating between main object and background automatically without entering any parameter.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

Sari

Hapsani

Adinugroho

et al. 2021

Int. J. Artif. Intell. Res

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Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.

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“…To avoid the painful biopsy process, skin cancer can be efficiently diagnosed non-invasively with help of suitable imaging modalities and image processing techniques [2]. The clinical diagnosis of skin lesions rely on four features namely asymmetry, border, colour and diameter [3].The conventional steps for diagnosing skin cancer from dermoscopy images using image processing comprises of pre-processing, segmentation, feature extraction, and classification [1]. The skin image collected from the image acquisition device (source) mostly contain noise artifacts due to non-uniform illumination of light and in order to remove those conditions, a handful pre-processing steps are mandatory [2,4].…”

Section: Introductionmentioning

confidence: 99%

Benign and Malignant Dermatoscopy Image Classification

Bobby

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Skin cancer is one of the life threatening diseases and there is mostly no chance of remission from skin cancer if diagnosed in the last stage. The three major types of skin cancers are basal cell carcinoma, squamous cell carcinoma, and melanoma. Among these three, melanoma skin cancer is dangerous and acute in nature. Dermatologists use various techniques for diagnosing the malignant, in which the popular and reliable clinical method is dermatoscopy. The manual inference of the disease condition from the dermatoscopy images requires intensive knowledge and experience in the related field. Also, there will be an unavoidable degree of variability in image analysis occurs as long as the diagnostics procedure relies on human visual perception. Thus recently, image processing and machine learning algorithms have been applied for the accurate diagnoses of skin cancers from the dermatoscopic images. Thus, the goal of the proposed work is to automatically segment and classify the dermatoscopy skin lesion image with the help of image processing and machine learning algorithms. The proposed approach classifies the skin lesion image as benign or malignant melanoma with 90% accuracy, 91% sensitivity, 86% specificity, and 93% precision.

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Early stage detection and classification of melanoma

Cited by 21 publications

References 5 publications

The Depth Estimation and Visualization of Dermatological Lesions: A Novel Methodology (Preprint)

The Depth Estimation and Visualization of Dermatological Lesions: A Novel Methodology (Preprint)

Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

Benign and Malignant Dermatoscopy Image Classification

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