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Background: Melanoma is a skin disorder caused by malignant degeneration of the pigment cells melanocytes. The disease is characterized by widespread discoloration with irregular and protruding edges. In treating cancer itself, radiotherapy technology, has been found. It is a method of treating cancer using high-energy radiation rays that are focused on cancerous tissue to kill or stop cancer cell division. This study aims to determine the effectiveness of radiotherapy technology as a treatment solution for melanoma skin cancer. The focuses of the study in this paper are the integumentary system and skin structure, melanoma skin cancer, the body's mechanisms for melanoma skin cancer, and radiotherapy as a treatment method. Methods: The research method used is literature study from various sources, to help understand the problem more deeply and completely. Conclusions: Melanoma, which attacks one of the body's integumentary systems, namely the skin, has a close relationship with homeostatic dysregulation and endocrine damage. However, radiotherapy method can be used to cure melanoma skin cancer by shooting X-rays and damaging the cancer cells and also reducing their spread to other parts of the organs. This method is effective because melanoma skin cancer is localized or has not spread to other organs. However, the use of radiotherapy can also have a negative impact on the body and can give unwanted side effects, so consideration is needed before using this method.

Melanoma skin cancer is derived from skin melanocytes and has a high risk of metastatic spread. The era of molecular genetics and next-generation sequencing has uncovered the role of oncogenic BRAFV600E mutations in many melanomas, validated the role of ultraviolet-induced DNA mutations in melanoma formation, and uncovered many of the molecular events that occur during melanoma development. Targeted therapies and immunotherapy have dramatically improved outcomes and provided an increased rate of cure for metastatic melanoma. This article reviews the formation of melanoma, the molecular events involved in melanoma growth and metastasis, and the biology underlying resistance to melanoma therapies.

Background This paper investigates the difference in survival of melanoma patients across stage and sex by utilising net survival measures. Metrics are presented at both the individual and population level. Methods Flexible parametric models were fitted to estimate life-expectancy metrics to be applied to a group of 104,938 subjects with a melanoma skin cancer diagnosis from 2000 to 2017. Period analysis was used for better predictions for newly diagnosed patients, and missing-stage information was imputed for 9918 patients. Female relative survival was assigned to male subjects to demonstrate the survival discrepancies experienced between sexes. Results At the age of 60, males diagnosed at the regional stage lose an average of 4.99 years of life compared to the general population, and females lose 4.79 years, demonstrating the sex variation in expected mortality. In 2017, males contributed 3545 more life years lost than females, and a potential 1931 life years could be preserved if sex differences in survival were eliminated. Conclusions This study demonstrates the survival differences across population subgroups as a result of a melanoma cancer diagnosis. Females experience better prognosis across age and stage at diagnosis; however, further investigation is necessary to better understand the mechanisms behind this difference.

Introdução: O consumo do café oferece várias ações benéficas no corpo humano, quando consumido em doses adequadas, dentre elas a ação anti-inflamatória, antioxidante e estimuladora do sistema nervoso central. Objetivo: O objetivo deste estudo foi avaliar o impacto do consumo de café na prevenção de melanoma. Material e Método: Foi realizada uma revisão bibliográfica nas bases de dados em saúde Medline, Lilacs e SciELO, utilizando os descritores em Ciências da Saúde (DeCS): nutrição; polifenóis; café; melanoma, nos idiomas português e inglês, considerando o período de junho de 2013 a junho 2020. Resultados: Foram encontrados 283 estudos, 111 foram incluídos e 172 excluídos por não atenderem os critérios de elegibilidade. De acordo com a literatura observou-se a capacidade da cafeína de induzir depleção reduzida de tióis e efeito pró-apoptótico seletivamente em células de melanoma, onde o café com cafeína foi constantemente associado a possível efeito protetor no melanoma, mas em altas doses pode estar associada ao desenvolvimento de outras doenças. Conclusão: Neste estudo de revisão, o café mostrou-se como um possível agente promissor no câncer de pele melanoma, auxiliando na melhora do prognóstico da doença. Palavras-chave: nutrição, polifenóis, café, melanoma.

Introduction: The use of The use of nanoparticles containing antioxidant and cytotoxic plant compounds can have a special place in the treatment of melanoma. The aim of this study was to evaluate the antioxidant and cytotoxic effects of pineapple fruit extract on skin cancer. Methods: In the present experimental study, liposomal vesicles were prepared using cholesterol, soy phosphatidylcholine, and polyethylene glycol, and pineapple fruit extract was loaded in liposomes. Physicochemical characteristics were evaluated using zeta sizer, FTIR and AFM. Finally, the toxicity of different concentrations of extract and liposome-containing extract was evaluated in A-375 melanoma cell line using MTT assay. DPPH test was used to evaluate the antioxidant properties of the extract and liposomes containing extract. Statistical analysis was performed using Excel and SPSS (Ver 22) software and Duncan and Student's T-tests were used for statistical conclusion. Results: According to this study showed the encapsulation efficiency of pineapple extract containing liposome, liposome size and its surface charge were 40%, 89.9 nm and -8.8 mV, respectively. FTIR analysis and AFM micrographs also confirm that there is no interaction of the extract with its nanosystem, the spherical morphology of the liposomes and its appropriate distribution and dispersion. The toxicity level of pineapple extract is higher when had been encapsulated rather than the non-encapsulated extract on the A-375 cell line. Conclusion: Pineapple fruit extract has cytotoxic effects on A-375 cell line and the present liposomal nanocarrier can be a suitable carrier for the delivery of the extract and inhibit the growth and proliferation of these cells.

The Melanoma is the deadliest skin cancer. It can be developed in any parts of the human body. The cancer disease can be cured if it is diagnosed early and proper treatment is taken. In cancer classification, there is a problem in handling the large data of cancer. Large data contains meaningless data and redundant data. Therefore, to overcome the problem, many computer approaches for classification have been proposed in the previous literature. This time, the clustering process for melanoma is conducted using Support Vector Machine and K-Means. Therefore, the purpose of this research is to identify and evaluate the performance of the accuracy of genes that contain melanoma skin cancer using the clustering algorithms.

Basal cell carcinoma, squamous cell carcinoma, and melanoma represent the three most common skin cancers that occur on the face. The most common surgical treatments for facial skin cancers are Mohs surgery and standard local excision. The effective utilization of either of these techniques is based on tumor and patient risk stratification incorporating known risk factors for occult invasion and local recurrence, combined with patient comorbidities, expectations, and desires. Best available evidence highlights multiple and consistent risk factors for each specific skin cancer type, and dictate local control rates reported in the literature. Recognizing gaps in the literature, we compare and review surgical treatment guidelines and data for standard local excision versus Mohs surgery for cutaneous nonmelanoma and melanoma skin cancer. This article serves as a resource for optimal therapeutic decision making for surgical management of skin cancer on the face.

Malignant melanoma is the utmost severe form of skin cancer, causing the vast majority of deaths related to skin cancer in humans. Although melanoma is treatable at its early stages at localized fo...

Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M "Melanoma Skin Cancer Detection using Image Processing and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf

Melanoma is a serious type of cancer. This belongs to the skin cancer category and develops from the abnormal growth of melanocytes on our skin. A controlled growth of melanocytes is benign in nature and develops normal lesions. An uncontrolled growth of melanocytes is malignant in nature and develops abnormal lesions on the body. These abnormal lesions are cancerous in nature and this condition is known as melanoma. The benign and malignant lesions can be differentiated with their shape, texture and color features. In this paper, melanoma skin cancer detection is done by the classification of benign and malignant skin lesions from dermoscopic images. These dermoscopic images are preprocessed and then the required part of the image is obtained by segmentation. The statistical texture features and shape features are extracted from the segmented image. These extracted features are provided as input to several classifiers and the classification of benign and malignant skin lesions has been done. As a result, the melanoma skin cancer is detected from the dermoscopic image. Among the classifiers employed, Support Vector Machine (SVM) classifier with a linear kernel produces good classification results with efficient performance metrics.

Malignant melanoma is a lethal human skin cancer that is not easily treatable through traditional medicines, surgeries, and therapies. Millions of cases are recorded annually to cure physiological skin defects by chemotherapy that causes several adverse effects and challenges. Moreover, pathogenic infections might aggravate infection with subsequent ulceration or cutaneous melanoma. Accordingly, we synthesized nanodrug by loading chemotherapeutic agent, Fluorouracil (5FU), onto the reduced graphene oxide (rGO). We then extracted arabinoxylan (ARX) from the husk of Plantago Ovata and functionalized it into carboxymethylarabinoxylan (CMARX) and loaded synthesized nanodrug. We have crosslinked CMARX/nanodrug with different amount of tetraethylorthosilicate (TEOS) to prepare nanocomposite hydrogel rGO-5FU-CMARX system for melanoma skin cancer care and treatment. These nanocomposite hydrogel systems rGO-5FU-CMARX have exhibited different physicochemical properties. These properties were analyzed through FTIR, SEM, water contact angle, swelling in different media (aqueous and PBS) and biodegradation in PBS media. The in-vitro activities, i.e., drug delivery via Franz diffusion, antibacterial against S. aureus and P. aeruginosa and the anticancer activities was performed against Uppsala 87 Malignant Glioma (U-87) cell lines. Moreover, rGO-5FU-CMARX nanocomposite hydrogels displayed different antimicrobial and anticancer activities based on different crosslinking. Hence, an inventive rGO-5FU-CMARX based nanocomposite hydrogel drug-delivery system was developed to treat malignant melanoma skin cancer after bacterial infections.

Melanoma is a form of skin cancer if detected earlier can be cured. Melanoma appears on skin as large mole or spot, which varies in colour and shape. Clinical diagnosis which involve ABCDE where A is the asymmetric shape of mole, B is border of mole which is blurred, C is colour of mole, D is diameter which determines size and E stands for evolving growth on skin layer. Use of Machine learning in dermatologist practice for detection of melanoma gives a prominent result. With advances in technology, computer vision images of melanoma are scanned and are classified with help of machine learning algorithms for specific determination of spot or mole to be melanoma or not. The features explored include colour, shape, size and elevation above skin which can be captured with computer vision. This prior detection helps for early cure for melanoma. Choosing the right algorithm and to understand the algorithm formula, tuning your algorithm for accuracy, optimization, time efficiency, process parameters in a given data set and model complexity. To predict melanoma skin cancer in early time we can use CNN as of which will give more accuracy.

Early identification of melanoma skin cancer is vital for the improvement of patients’ prospects of five year disease free survival. The majority of malignant skin lesions present at a general practice level where a diagnosis is based on a clinical decision algorithm. As a false negative diagnosis is an unacceptable outcome, clinical caution tends to result in a low positive predictive value of as low at 8%. There has been a large burden of surgical excisions that retrospectively prove to have been unnecessary.This paper proposes a method to identify melanomas in dermoscopic images using a convolution neural network (CNN). The proposed method implements transfer learning based on the ResNet50 CNN, pretrained using the ImageNet dataset. Datasets from the ISIC Archive were implemented during training, validation and testing. Further tests were performed on a smaller dataset of images taken from the Dermnet NZ website and from recent clinical cases still awaiting histological results to indicate the trained network’s ability to generalise to real cases. The 86% test accuracy achieved with the proposed method was comparable to the results of prior studies but required significantly less pre-processing actions to classify a lesion and was not dependant on consistent image scaling or the presence of a scale on the image. This method also improved on past research by making use of all of the information present in an image as opposed to focusing on geometric and colour-space based aspects independently.

Skin cancer is deemed as the lethal type of cancer threatening worldwide with an increase in mortality rate per year. The growing incidences of melanoma skin cancer have introduced numerous treatment options. However, surgical treatment remains the basis for treating skin cancers. Automated skin cancer detection still remains a challenging task in the current scenario. Prior diagnosis of skin cancer requires computer-aided diagnosis. Computerized methods for analyzing images in dermoscopy are a subject of interest as dominant information regarding skin lesions can be retrieved. This paper has discussed the state of art techniques employed in CAD systems by providing domain facets of melanoma accompanied by efficient methods utilized in every phase. The phases comprise image preprocessing techniques, extraction, and selection of significant features, segmentation methods, and classification approaches for the identification of skin lesions. Inapplicability and future trends are discussed in the domain of research.

The development of cameras in smartphones is possible as a point of care for early detection of cancer. Early detection using smartphones is carried out by giving the smartphone the ability to recognize objects with skin cancer characteristics. The convolution neural network (CNN) is often used in disease detection and classification. However, the CNN method requires high computing capability and a large memory that is difficult to perform on smartphones. In this paper, the MobileNet v2 and Faster R-CNN methods are utilized and executed on an Android-based application that can detect skin cancer. Both proposed architectures were trained to recognize actinic keratosis and melanoma skin cancer targets on images. The dataset used was 600 images, divided into two classes, actinic keratosis images and melanoma images with no attention for gender, age, or additional factors. In this study, an android app was developed to utilize the smartphone camera for skin cancer detection. The Faster R-CNN and MobileNet v2 models were implemented as an intelligent system for the screening. Two testing methods were performed in this study, the Jupyter notebook and android camera. Based on the experiment result, Faster R-CNN obtained higher accuracy when testing using the Jupyter, and MobileNet v2 got the same high accuracy when applying on a smartphone.

In a recent paper, Zioutas and Valachovic (2018) claim that dark matter is responsible for a significant fraction of the melanoma skin cancer. This conclusion is drawn from their observation of a significant correlation between skin melanoma incidence in the US and the inner planets positions (especially those of Mercury and Earth). Here, I present a number of objections to their interpretation. Some (but not all) of the counterarguments are based on the analysis of a larger dataset from the same source, considering more cancer types and separating by patient attributes, such as race. One of the counterarguments is that if the melanoma fluctuations with periods similar to planetary orbits were produced by dark matter density enhancements on Earth, then we would have to conclude that the black population is somehow immune to dark matter, a conclusion that seems incompatible with the current Weakly-Interacting Massive Particles (WIMP) paradigm. Interestingly, some periodicities are present in the data, including the ones reported by Zioutas and Valachovic, but I argue that they must have a societal rather than astronomical origin.

Melanoma skin cancer is one of the deadliest cancers today, the rate of which is rising exponentially. If not detected and treated early, it will most likely spread to other parts of the body. To properly detect melanoma, a skin biopsy is required. This is an invasive technique which is why the need for a diagnosis system that can eradicate the skin biopsy method arises. It is observed that the proposed method is successfully detecting and correctly classifying the malignant and non-malignant skin cancer. Finally, a neural network is used to classify benign and malignant images from the extracted features. Keywords: Melanoma, non-invasive, skin lesion, neural network.

abdulhamid, idris/0000-0002-6272-8318; Sahiner, Ahmet/0000-0002-4945-2476; rahebi, cevat/0000-0001-5418-9601 WOS:000530345200009 PubMed: 32351994 In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed by utilising Bezier curves. This function achieves all filled function properties. The proposed optimization method is applied to find the threshold values in melanoma skin cancer images. The proposed algorithm is implemented on PH2, ISBI2016 challenge, and ISBI 2017 challenge datasets for melanoma segmentation. The results show that the proposed algorithm exhibits high accuracy, sensitivity, and specificity compared with other methods.

This paper examines about a type of skin malignant growth which is melanoma. There are numerous types of skin malignancy, for example, Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC) and Melanoma. In which the deadliest type of skin disease is the Melanoma. Demise pace of melanoma has expanded among skin malignant growth patients and it is hazardous. The death rate is highest among among middle aged and elderly individuals. It is seen as risky when it develops beyond the dermis of the skin. This paper deals with a survey on a few computerized analysis procedures for diagnosing melanoma. These procedures extract different parameters, for example, shape, size, surface, shading and different properties of lesions which is utilized for additional exploration. The precise skin affected region which is the skin lesion or area of intrigue will be taken out for automated medical procedure. The ATLAS dataset or PH2 dataset pictures are considered for investigation in the majority of the papers.

Melanoma is one of the most treacherous forms of skin cancer. An automatic melanoma detection system (AMDS) is an image based melanoma skin cancer detection system which detects the melanoma cancer from the captured image of the infected skin. AMDS is helpful in the early detection of skin cancer. The manual detection is time-consuming and depends upon factors like clinical tests, and knowledge of dermatologist. The detection of melanoma is an exigent task using manual processing. Therefore, an AMDS is needed. Macroscopic and dermoscopic images can be used for the analysis of melanocytic and non-melanocytic lesions. It is possible to detect melanoma in non-invasive manner using lesion characteristics like color, shape, and structure. In this paper, we present a comprehensive review of the state-of-the-art techniques for AMDS. We categorize different AMDS techniques according to features used for each stage, and compare them in terms of accuracy, sensitivity and working principle along with the suggestions for future scope for detection of melanoma.

Skin cancer is a high burden disease with a high impact on global health. Conventional therapies have several drawbacks; thus, the development of effective therapies is required. In this context, nanotechnology approaches are an attractive strategy for cancer therapy because they enable the efficient delivery of drugs and other bioactive molecules to target tissues with low toxic effects. In this review, nanotechnological tools for skin cancer will be summarized and discussed. First, pathology and conventional therapies will be presented, followed by the challenges of skin cancer therapy. Then, the main features of developing efficient nanosystems will be discussed, and next, the most commonly used nanoparticles (NPs) described in the literature for skin cancer therapy will be presented. Subsequently, the use of NPs to deliver chemotherapeutics, immune and vaccine molecules and nucleic acids will be reviewed and discussed as will the combination of physical methods and NPs. Finally, multifunctional delivery systems to codeliver anticancer therapeutic agents containing or not surface functionalization will be summarized.

Skin cancer is the most common cancers in the last years, especially in the human body; the Melanoma is the most destructive type of skin lesions. Detect cancer is important at the initial stage, but only an expert dermatologist can detect which one is non-melanoma and melanoma. Computer-aided diagnosis (CADs) application to skin cancer is relatively understudied. The purpose of this paper is the automated detection of Melanoma via digital image processing. In this project, the algorithm consists of automatic ABCD (asymmetry, border irregularity, colour, and dermoscopic structure) rule of dermoscopy lesions images is implemented. Before that, we use hair removal as a pre-processing step which is based on morphological filter and thresholding. Finally, the lesions are classified as either melanoma or benign. The used dataset is containing 200 dermoscopic images, where 120 are benign lesions and 80 malignant melanomas. The proposed method shows an accuracy of 93.2%, 92.59% specificity, and 90.15% sensitivity.

Skin cancer exists in various forms like Melanoma, Basal and Squamous Cell Carcinoma among which Melanoma is the most hazardous and unpredictable. In this paper, we implement an image processing technique for the early detection of Melanoma Skin Cancer using MATLAB which is easy for use as well as detection of Melanoma skin cancer. There are two stages of detection, the first stage is a simple questionnaire which consists of all the common symptoms faced by melanoma affected person and if the report of the first stage comes out to be positive, the patient can go for the second stage, where the input to the system is the lesion skin image. Further, K-means segmentation is used to segment the images followed by feature extraction. The fetched parameter values are, therefore, used to determine whether the particular patient is suffering from skin cancer or not.

Melanoma classification on dermoscopy skin images is a demanding work as a result of the low contrast of the lesion images, the intra-structural variants of melanomas, the much visually likeliness level of whether melanoma or non-melanoma lesions, and the covering of hair and ruler marker artifacts. In this study, the malignant melanoma skin cancer classification system is proposed with the aid of correctly classify melanoma skin cancer. The system involves three main steps: segmentation, feature extraction and classification. Ahead of the segmentation process, the preprocessing skin lesion images is processed for getting rid of the covered hair artifacts. In the segmentation step, the input preprocessed lesion image is segmented by using the proposed texture filter-based segmentation method. Then, the extraction of features with the underlying ABCD (Asymmetry, Border, Color, Differential Texture) dermatology rules using shape, edge, colored and textural features are computed from the segmented region. Lastly, the extracted features are classified to identify if the skin image is malignant melanoma or non-melanoma with the use of bag tree ensemble classifier. The system performance is evaluated with the use of the benchmarking datasets: PH2 dataset, ISBI2016 dataset and ISIC2017 dataset. According to the experimental results, the proposed design allows for both reliable classification of real world dermoscopy images and feasible operation time with today’s standard PC computing platforms. To address the class imbalance in the dataset and to yield the improved classification performance, the experiments are also analyzed not only on original imbalanced dataset but also on balancing datasets: undersampled and oversampled datasets. The system works well and provides both high sensitivity and specificity according to the experimental results on the oversampled dataset with bag tree ensemble classifier to leading to statistically better performance compared to original imbalanced dataset.

Melanoma is one of the most dangerous skin cancers in the world. It accounts for 55% of all deaths associated with skin cancer. Researchers believe that skin cancer increases the risk of other cancers if not diagnosed early. Therefore, prompt and timely diagnosis of this disease is very important for the successful treatment of the patient. This system can detect melanoma lethal carcinoma from other skin lesions without the need for surgery, with a low cost, accuracy of about 98.88% and specificity 99%. In this article, a new, intelligent and accurate software (Delphi) system has been used to diagnose melanoma skin cancer. To detect malignant melanoma, the ABCDT rule, asymmetry (A), boundary (B), color (C), diameter (D) and textural variation (T) of the lesion are calculated and finally, an artificial neural network (ANN) is used to obtain an accurate result. The ANN with Multi-Layer Perceptron (MLP) contains the five extraction Characteristics (ABCDT) of lesions is used as inputs, two hidden layers, and two outputs. Very good results were obtained using this method. It was observed that for a dataset of 180 dermoscopic lesion images including 80 malignant melanomas, 20 benign melanomas and 80 nevus lesions. Due to its automatic recognition and ability to be installed on a computer, this system can be very useful for dermatologists as well as the general public.

Melanoma skin cancer is one of the most fatal skin cancer forms but can be easily cured if detected early enough. In this paper, we have presented a solution for detecting early stage melanoma skin cancer using a convolution neural network. Image processing is applied on raw images before feeding them to the neural network. We have utilized 514 dermoscopic images from the International Skin Imaging Collaboration (ISIC) archive for training and validating our model. The experimental results obtained via this process were promising and would immensely help dermatologists in early stage detection of melanoma.

Melanoma cancer is a type of skin cancer and is the most dangerous one because it causes the most of skin cancer deaths. Melanoma comes from melanocyte cells, melanin-producing cells, so that melanomas are generally brown or black coloured. Melanomas are mostly caused by exposure to ultraviolet radiation that damages the DNA of skin cells. The diagnoses of melanoma cancer are often performed manually by using visuals of the skilled doctors, analyzing the result of dermoscopy examination and match it with medical sciences. Manual detection weakness is highly influenced by human subjectivity that makes it inconsistent in certain conditions. Therefore, a computer assisted technology is needed to help classifying the results of dermoscopy examination and to deduce the results more accurately with a relatively faster time. The making of this application starts with problem analysis, design, implementation, and testing. This application uses deep learning technology with Convolutional Neural Network method and LeNet-5 architecture for classifying image data. The experiment using 44 images data from the training results with a different number of training and epoch resulted the highest percentage of success at 93% in training and 100% in testing, which the number of training data used of 176 images and 100 epochs. This application was created using Python programming language and Keras library as Tensorflow back-end.

Melanoma is the deadliest form of skin cancer, accounting for about 75% of deaths related to this type of disease. Fortunately, melanoma early detection can increase the survival rate of victims considering that melanoma skin cancer is often visible to patients and physicians. However, recommended self-examinations or physician-directed exams are not significantly reducing melanoma deadly cases due to the absence of knowledge of the patients or the lack of access to well-trained physicians. Based on that, this paper proposes a computer aided diagnosis system that detects melanoma skin cancer using dermatoscopy images, image processing techniques, and machine learning algorithms. Our main goal is to create a cheap, relatively accurate, and easy-to-use system available as an initial procedure to detect melanomas. The evaluation of the designed system using 748 dermatoscopy images shows sensitivities around 98%, when a simple feature-extraction stage is applied and a classifier based on support vector machines is utilized.

The aim of this paper is to extract some distinct geometric features from dermoscopy images to classify benign and malignant melanomas. To avoid skin biopsy which is an invasive technique, diagnosis of melanoma skin cancer from dermoscopy images was developed. It is a very challenging task due to some reasons. Firstly, high degree of intraclass variation exists among melanoma images while low interclass variation is found between melanoma and non-melanoma images. Secondly, benign and malignant melanoma images are visually similar to a great extent. And finally noises like hair are always present in skin images which make difficult to analyse the images. In this work, we used fundamental ABCD rule to detect malignant melanoma and benign lesion based on quantitative measures. In our proposed technique, we extracted a new feature which is the difference between maximum and minimum Feret diameters of the best fit ellipse to skin lesion. This discriminative feature alone classified the melanomas with 86.5% accuracy. In our approach, we applied the feature extraction block containing all parameters to 200 images and the overall accuracy of 98% was achieved to detect malignant and benign melanoma from the images. A Back-propagation Neural Network (BNN) model was developed and eventually used as a classifier in this proposed method.

Melanoma is a type of skin cancer. Melanoma skin cancer is made up of abnormal pigment cells (melanocytes). Melanocytes are located at the bottom of the epidermis. These cells make melanin, which spreads to the top of the epidermis and gives skin its colour. Melanoma is usually brown or black in colour because the cell still makes melanin. Melanoma is more dangerous than other skin cancers because it more likely to spread if it is not found early. However, most melanomas about 84 out of 100 are found early before they have spread and so are likely to be cured with treatments. Melanoma can occur on any skin surface. In men, it’s often found on the skin on the head, on the neck, or between the shoulders and the hips. In women, it’s often found on the skin on the lower legs or between the shoulders and the hips. Melanoma is rare in people with dark skin. When it does develop in people with dark skin, it’s usually found under the fingernails, under the toenails, on the palms of the hands, or on the soles of the feet. Although one of the less common types of skin cancer, melanoma is considered the most serious type of skin cancer because it is more likely to spread to other parts of the body, especially if not detected early. The earlier melanoma is found, the more successful treatment is likely to be. Despite the many investigations in this field and a rapidly growing knowledge base, classification according to specific mutational profiles is not yet validated. Further investigations are required for validation and refinement, and to possibly identify additional factors.