Skin Cancer Detection System Research Articles

Skin cancer detection using dermoscope images

Skin cancer is an abnormal growth of skin cells which develops on the body when exposed to the sunlight. Most of the skin cancers are curable when detected at early stage. Early diagnosis can help to treat the patients effectively. Formal method for diagnosing skin cancer is biopsy. In this paper, skin cancer detection system is developed using LabVIEW. The proposed method uses vision development module in LabVIEW to process dermoscope images. Preprocessing steps such as median filtering and contrast stretching is performed for noise removal and image enhancement. Threshold based segmentation is adopted to segment the affected area. Features such as Asymmetry, Border, Color and Diameter (ABCD) are extracted. The presence of cancer is detected using ABCD rule.

Skin Lesion Detection using Texture Based Segmentation and Classification by Convolutional Neural Networks (CNN)

Skin cancer is one of the dangerous cancers like breast cancer, brain tumour, and lung cancer. The detection of a skin lesion is melanoma or nonmelanoma is a very crucial issue. The earlier detection of melanoma is one of the best solutions for this issue. There is a various technique for detecting the skin lesion. Because of the technology advancement earlier detection of the skin lesion is possible. Malignant melanoma is a very harmful melanoma it is the cancerous cell that will lead to growth and that can be a mole in different colours red, black and brown. Skin lesion segmentation from dermoscopic images is a very challenging task nowadays because of the contrast of those images. there are various techniques for detecting the skin cancer base on the characteristics of the images shape, colour, textures. We proposed a system for skin cancer detection using texture-based segmentation and classification using Convolutional Neural Network. GLCM (Gray Level Co-occurrence Matrix) matrix is exacting the features from an image. And used Neural network tool for checking the accuracy of training network. Nowadays Deep Learning technique is very popular for classification of images. CNN is one of the techniques of Deep Learning. The proposed work will help in classification of skin lesion. Model will helpful for dermatologists for classifying melanomas.

An Enhanced and Automatic Skin Cancer Detection using K-Mean and PSO Technique

Scientists have been trying to implement traditional methods around the world, particularly in developing countries, to reduce the death rate of skin cancer in humans. The scientific term is named as melanoma. But this effort always working hard as the system is costly, the low availability of experts and the conventional telemedicine. There are three types of skin cancer: basal cell cancer (BCC), squamous cell cancer, and melanoma. More than 90% of human is affected by ultraviolet (UV) radiation exposed to the sun. In this research, a skin cancer detection system (BCC) is designed in MATLAB. The images going to different processes such as Pre processing, feature extraction and classification. In pre-processing K-mean clustering is applied to determine the foreground and background of an image, since some part of background appear in the image after K-mean. Therefore, to resolve this problem Particle Swarm optimization (PSO) is applied. The segmented image features are extracted using Speed Up Robust Features (SURF), this helps to enhance the quality of the image. The Artificial neural network (ANN) is trained on the basis of these extracted features. To determine the efficiency of the system, the images are tested and performance parameters are measured. The detection accuracy determined by this model is about 98.7 5 is obtained.

Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models

In this research, we propose an intelligent decision support system for skin cancer detection. Since generating an effective lesion representation is a vital step to ensure the success of lesion classification, the discriminative power of different types of features is exploited. Specifically, we combine clinically important asymmetry, border irregularity, colour and dermoscopic structure features with texture features extracted using Grey Level Run Length Matrix, Local Binary Patterns, and Histogram of Oriented Gradients operators for lesion representation. Then, we propose two enhanced Particle Swarm Optimization (PSO) models for feature optimization. The first model employs adaptive acceleration coefficients, multiple remote leaders, in-depth sub-dimension feature search and re-initialization mechanisms to overcome stagnation. The second model uses random acceleration coefficients, instead of adaptive ones, based on non-linear circle, sine and helix functions, respectively, to increase diversification and intensification. Ensemble classifiers are also constructed with each base model trained using each optimized feature subset. A deep convolutional neural network is devised whose hyper-parameters are fine-tuned using the proposed PSO models. Extensive experimental studies using dermoscopic skin lesion data, medical data from the UCI machine learning repository, and ALL-IDB2 image data are conducted to evaluate the model efficiency systematically. The results from empirical evaluations and statistical tests indicate the superiority of the proposed models over other advanced PSO variants and classical search methods pertaining to discriminative feature selection and optimal hyper-parameter identification for deep learning networks in lesion classification as well as other disease diagnosis.

Novel ABCD Formula to Diagnose and Feature Ranking of Melanoma

A prototype of skin cancer detection system for melanoma diagnoses in early stages is very important. In this paper, a novel technique is proposed for Skin malignant growth identification based on feature parameters, color shading histogram, to improve the diagnosis method by optimizing the ABCD formula. Features are extracted like Shape, Statistical, GLCM texture, Color, Wavelet transform, Texture. Once the features are extracted we found the most prominent features by assigning a rank. We have calculated parameters such as sensitivity, specificity, accuracy for checking the imperceptibility and robustness of the proposed approach. Also, Correlation analysis is made between traditional and proposed TDS equation using Karl Pearson’s method.

Skin lesion segmentation using deep convolution networks guided by local unsupervised learning

Automatic localization of skin lesions within dermoscopy images is a crucial step toward developing a decision support system for skin cancer detection. However, segmentation of the lesion image can be challenging, as these images possess various artifacts distorting the uniformity of the lesion area. Recently, deep convolution learning-based techniques have drawn great attention for pixel-wise image segmentation. These deep networks produce coarse segmentation, and convolutional filters and pooling layers result in segmentation of a skin lesion at a lower resolution than the original skin image. To overcome these drawbacks, we have proposed a superpixel-based fine-tuning strategy to effectively utilize the characteristics of the skin image pixels to accurately extract the border of the lesion. Our proposed approach not only learns a global map for skin lesions, but also acquires the local contextual information, such as lesion boundary. It can, therefore, accurately segment lesions within a given skin image, even in the presence of fuzzy boundaries and complex textures. To evaluate the performance of our proposed method, experiments have been conducted using the 2016 International Symposium on Biomedical Imaging dataset, and these experiments suggest the effectiveness of the proposed method.

Pigment network-based skin cancer detection.

Diagnosing skin cancer in its early stages is a challenging task for dermatologists given the fact that the chance for a patient's survival is higher and hence the process of analyzing skin images and making decisions should be time efficient. Therefore, diagnosing the disease using automated and computerized systems has nowadays become essential. This paper proposes an efficient system for skin cancer detection on dermoscopic images. It has been shown that the statistical characteristics of the pigment network, extracted from the dermoscopic image, could be used as efficient discriminating features for cancer detection. The proposed system has been assessed on a dataset of 200 dermoscopic images of the `Hospital Pedro Hispano' [1] and the results of cross-validation have shown high detection accuracy.

The Beneficial Techniques in Preprocessing Step of Skin Cancer Detection System Comparing

Automatic diagnostics of skin cancer is one of the most challenging problems in medical image processing. It helps physicians to decide whether a skin melanoma is benign or malignant. So, determining the more efficient methods of detection to reduce the rate of errors is a vital issue among researchers. Preprocessing is the first stage of detection to improve the quality of images, removing the irrelevant noises and unwanted parts in the background of the skin images. The purpose of this paper is to gather the preprocessing approaches can be used in skin cancer images. This paper provides good starting for researchers in their automatic skin cancer detections.

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques - combines computer engineering concepts with areas like dermatology and oncology. Its objective is to discern images of skin cancer, specifically melanoma, from others that show only common spots or other types of skin diseases, using image recognition. This work makes use of the ABCDE visual rule, which is often used by dermatologists for melanoma identification, to define which characteristics are analyzed by the software. It then applies various algorithms and techniques, including an ellipse-fitting algorithm, to extract and measure these characteristics and decide whether the spot is a melanoma or not. The achieved results are presented with special focus on the adaptive decision-making and its effect on the diagnosis. Finally, other applications of the software and its algorithms are presented.

Automatic detection of irregular borders in melanoma and other skin tumors

An irregularity index previously developed is applied to detect irregular borders automatically in skin tumor images, particularly malignant melanoma. The irregularity index is used to classify various tumor borders as irregular or regular. This procedure processes tumor images with borders automatically determined by a radial search algorithm previously described. Potential use of this algorithm in an in vivo skin cancer detection system and errors expected in the use of the algorithm are discussed.