International Journal of Computer Science and Mobile Computing Implementation of ANN Classifier using MATLAB for Skin Cancer Detection (original) (raw)
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Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features
International Journal of Trend in Scientific Research and Development, 2018
Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various...
Neural Network Based Detection of Melanoma Skin Cancer
2013
Skin cancer is the deadliest form of cancers in humans. It is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most unpredictable. Detection of melanoma skin cancer in the earlier stage is very critical. In this paper, we explain the method for the detection of Melanoma Skin Cancer using Segmentation. The input to the system is the Dermoscopic Image and then apply and then by applying novel image processing techniques. The different stages of detection involves-collection of Dermoscopic Images, filtering the images by using Dull Razor filtering for removing hairs and air bubbles in the image, converting to gray scale, contrast enhancement, noise filtering, segmenting the images using Maximum Entropy Threshold. Feature Extraction technique used is Gray Level Co-occurrence Matrix (GLCM).It is a powerful tool for image feature extraction by mapping the gray level co-occurrence probabilities based on spatial relations of pixels in di...
Computer Aided System for Diagnosis of Skin Cancer Using Classification
2018
Skin cancer is the increasing growth of abnormal skin cells. It occurs when unrepaired DNA damage to skin cells begins mutations, or genetic defects, that lead the skin cells to multiply rapidly and form malignant tumors. Malignant melanoma is considered as one of the most dangerous type of skin cancers as it increases the mortality rate. Computer-aided diagnosis systems can help to detect melanoma early. In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks and NB which are used perform a classification of the different kinds of moles.
An artificial neural network approach for detecting skin cancer
TELKOMNIKA Telecommunication Computing Electronics and Control, 2019
This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature extraction method of the first order for feature extraction based on texture in order to get high degree of accuracy with method of classification using artificial neural network (ANN). The method used is training and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of melanoma images and 17 as suspected melanoma images.
this paper proposes an automatic skin cancer (melanoma) classification system. The input for the prosed system is a collected data images, it followed by different image processing procedures to enhance the image properties. Two segmentation methods used to identify the normal skin cancer from malignant skin and to extract the useful information from these images that passed to the classifier for training and testing. The features used for classification is the coefficients created by Wavelet decompositions and simple wrapper curvelet. Curvelet is suitable for the image that contains oriented texture and cartoon edges. Recognition accuracy of the three layers back-propagation neural network classifier with wavelet is 51.1% and with curvelet is 75. 6% in digital images database.
International Journal of Advances in Applied Sciences (IJAAS), 2022
Skin cancer is one of the most dangerous types of cancer. Some types of this cancer lead to death, so cancer must be discovered and indexed to avoid its spread through initial detection in the impulsive stage. This paper deals with the detection and indexing of different types of melanomas using an artificial neural network (ANN) depending on the international skin imaging collaboration (ISIC) 2018 dataset that was used. The pre-processing is the most important part because it formulates an image by insolated the cancer part from the skin image. It consists of four stages, removable, cropping, thinning, and normalization. This phase has been used to eliminate all the undesirable hair particles on the image lesion. The cropped image transforms into frequency domain coefficients using discrete cosine transform (DCT), discrete wavelet transform (DWT), and gradient transform for sub-band images to extract its feature. The statistical feature extraction is implemented to minimize the size of data for ANN training. The experimental analysis used dataset ISIC 2018 consisting of seven different types of dermoscopic images (this paper deals with four types only). For classification purposes, ANN was implemented and the accuracy obtained is about 88.98% for DWT, 85.44% for sub-band DCT, and 76.07% for sub-band gradient transform.
IMPLEMENTATION OF SUPERVISED LEARNING FOR MELANOMA DETECTION USING IMAGE PROCESSING
Among the different types of skin cancers, Melanoma is one of the most threatening type of cancer. This cancer is most often caused due to over exposure to ultraviolet radiation from the sun which causes unrepaired DNA damage to skin cells which further develops into cancerous tumours. This unrepaired damage to the skin usually affects the melanocytes, which are skin cells containing a pigment called melanin which is responsible for the colour of the skin, hence the name melanoma. If melanoma is recognised in the early stages it is proven to be curable. If not, the cancer advances and spreads to all other parts of the body and becomes incurable leading to death. One of the traditional methods of analysing melanoma is biopsy, which is a painful and time consuming process. To overcome this we have implemented a computer aided method for automatic melanoma detection and classification of Dermoscopic skin images with the help of Digital Image Processing and Artificial Intelligence. This paper proposes that using artificial intelligence for Melanoma detection increases the accuracy of classification.
Malignant Melanoma Detection Based on Machine Learning Techniques : A Survey 1
2016
Skin cancer is one of the most growing types and dangerous cancer in the world; the important of these cancers are malignant melanoma. The early diagnosis of malignant melanoma is a critical issue for dermatologists. In this paper, we present an overview of recent the state of the art in Computer-aided detection/diagnosis (CAD) systems in identifying and diagnosing malignant melanoma of dermoscopy images and describe its steps starting with image acquisition, preprocessing; and finishing with malignant melanoma classification of dermoscopic images. The comparative study shows that the most common methods for features extraction are the Discreet Wavelet Transform (DWT) and the method which combines both texture and color features resulting in output of very high accuracy. The methods for the classification:K-Nearest Neighbor, Artificial Neural Networks, and Support Vector Machines are very well in the range [%90 –% 97, 5].
The Melanoma Skin Cancer Detection and Classification Using Image Processing
2020
The most common type of cancer is the skin cancer in human being. It can be benign and malignant. There are medical methods to detect it but that consumes more time. So, computer-based application needs to be developed to detect this disease in its early stages in order to augment the patient’s survival likelihood. The aim of this paper to develop a simple and capable method to detect the melanoma. The proposed methods contain following stages, preprocessing, segmentation, feature extraction and classification. The accuracy of proposed method is 96.7% which shows its reliability. Index Terms Pre-Processing, segmentation, feature extraction, classification, image processing.