Recognition Of Skin Cancer Research Articles

Skin cancer recognition using SVM image processing technique

According to some estimates, skin cancer is the leading cause of death for people in the modern period. Although it can happen anywhere on the body, the majority of the time, this sort of cancer targets particular body regions that are more prone to being exposed to light. Skin cells proliferate in an erratic or patchy manner as a symptom. The majority of skin cancers can be treated if they are discovered early. Therefore, a patient’s life can be saved by promptly and precisely diagnosing skin cancer. Modern technology has made it feasible to detect skin cancer in its earliest stages. Skin cancer can be diagnosed systematically using the biopsy procedure (1). Skin cells are removed and supplied as a sample, following which they are evaluated in several laboratories. It’s an extremely unpleasant and time-consuming process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on support vector machine (SVM). It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called SVM are used in the identification process. In particular, epiluminescence microscopy is utilized using an image and several preprocessing techniques which are used in the reduction of sound artifacts and improve the quality of images. Segmentation is done by using certain thresholding techniques like Otsu. The gray level co-occurrence matrix technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The supervised learning model called SVM is used to distinguish data sets. It determines whether a picture is cancerous or not.

Multimodal Analysis of Unbalanced Dermatological Data for Skin Cancer Recognition

Multimodal Analysis of Unbalanced Dermatological Data for Skin Cancer Recognition

Automated Malignant Melanoma Classification Using Convolutional Neural Networks

This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %.

Efficient and Low-Cost Skin Cancer Detection System Implementation with a Comparative Study Between Traditional and CNN-Based Models

Medical image classification is an essential task in the field of combining medical applications with Artificial Intelligence. This study is carried out to introduce an accurate, precise method for skin cancer recognition. This research investigates the performance of classifying skin cancer dataset HAM10000 using ResNet50, MobileNet, and the traditional Support Vector Machines (SVM) model. The dataset combines seven cancer types: Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanoma, Melanocytic Nevus, and Vascular Lesion. The SVM classifier is designed to employ a Histogram of Oriented Gradient (HOG) features with Principle Component Analysis (PCA). Moreover, the Synthetic Minority Oversampling Technique is used to balance the dataset. Additionally, six conventional machine learning (ML) methods are used to compare the results with the calculation of precision, recall, F1 Score, and accuracy. The results confirm that the SVM method outperforms the other algorithms with an accuracy of 99.15%. The novelty contribution of this research activity is mainly based on the development of a high accuracy, low computational complex machine method for skin cancer types recognition in the domain of medical image classification.

Do we practice and do we preach? A survey of Irish interns regarding knowledge, attitudes, personal and professional practices of skin cancer detection.

We undertook a study of skin cancer (SC) recognition and health promotion among interns in an Irish hospital. While the knowledge on SC was good, the majority of the interns did not practice safe sun behaviours themselves, and the majority did not feel confident in diagnosing skin lesions or SC. Most had never performed a skin examination on patients, or discussed sun protection or skin self examination with their patients.

Skin Cancer Lesions Classification Using Probabilistic Neural Network

One of the most frequent types of cancer is skin cancer. Skin cancer is one of a deadly disease caused due to the abnormal skin cell proliferation in the epidermis resulting in the formation of mass called as Tumor. The formed tumor can be cancerous known as Malignant, that is it can raise and can open out to the rest of the body and the other is non-cancerous tumor known as benign which can raise but it does not open out to the rest of the parts. Skin cancer is mainly caused because of exposure of skin to the harmful UV(Ultraviolet) radiation. If skin cancer is detected in early stage then there are high chances for successful skin cancer treatment. In this paper we are describing a model which uses deep learning technique and some Image processing techniques for distinguishing of skin cancer as benign tumor or malignant tumor. The proposed model gives results in less time and eliminates the cost compared to the formal strategy that is Biopsy which is used for skin cancer recognition. The proposed model uses image processing techniques like Pre-processing, different Segmentation techniques, Feature Extraction and deep learning technique PNN (Probabilistic Neural Network) is used for classification of the skin image.

Assessing individual patients' knowledge of benign versus malignant skin lesions in the dermatology clinic population.

Background:Skin cancer education targeted to patients’ needs is a goal of practicing dermatologists. Data regarding dermatology patients’ baseline knowledge regarding skin cancer could aid clinicians in tailoring education efforts.Objective:To help quantify existing patients’ existing visual recognition of skin cancer and common benign lesions, with the goal of helping to provide more targeted and meaningful education to patients.Methods:Two hundred forty-four adult patients from the dermatology clinics at University of Oklahoma and Loyola University Chicago were surveyed using digital images and questions regarding personal and family history of skin cancer, sun protection practices and sun protection knowledge.Results:Of the 244 subjects, 43% percent had a positive personal history of skin cancer, 40% had a positive family history. Scores differed minimally by personal history of skin cancer (p = .37) but differed more markedly by family history of skin cancer (p = .02).Limitations:Lack of generalizability to the general public, age range of subjects.Conclusions:There are knowledge gaps within the dermatology patient population regarding common benign and malignant skin lesions.

Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application

Although many efforts have been made through past years, skin cancer recognition from medical images is still an active area of research aiming at more accurate results. Many efforts have been made in recent years based on deep learning neural networks. Only a few, however, are based on a single deep learning model and targeted to create a mobile application. Contributing to both efforts, first we present a summary of the required medical knowledge on skin cancer, followed by an extensive summary of the most recent related works. Afterwards, we present 11 CNN (convolutional neural network) candidate single architectures. We train and test those 11 CNN architectures, using the HAM10000 dataset, concerning seven skin lesion classes. To face the imbalance problem and the high similarity between images of some skin lesions, we apply data augmentation (during training), transfer learning and fine-tuning. From the 11 CNN architecture configurations, DenseNet169 produced the best results. It achieved an accuracy of 92.25%, a recall (sensitivity) of 93.59% and an F1-score of 93.27%, which outperforms existing state-of-the-art efforts. We used a light version of DenseNet169 in constructing a mobile android application, which was mapped as a two-class model (benign or malignant). A picture is taken via the mobile device camera, and after manual cropping, it is classified into benign or malignant type. The application can also inform the user about the allowed sun exposition time based on the current UV radiation degree, the phototype of the user’s skin and the degree of the used sunscreen. In conclusion, we achieved state-of-the-art results in skin cancer recognition based on a single, relatively light deep learning model, which we also used in a mobile application.

A machine learning model for skin disease classification using convolution neural network

Melanoma is a skin disease that tends to be lethal. It occurs when melanocytes develop in an uncontrolled manner. Melanoma goes under a few different names, including malignant melanoma. The incidence of melanoma is at its highest level ever recorded in both Australia and New Zealand. It is estimated that one in every 15 white New Zealanders will indeed be diagnosed with melanoma at some point in their lives. Aggressive malignancy was the third most common kind of cancer in men and women in 2012, respectively. Melanoma can develop at any age in adults, but it is highly unusual in children and teenagers. It is hypothesized that the first step in developing melanoma is an unregulated multiplication of melanocytic stem cells that have been genetically altered. The survival rate can significantly increase if melanoma is identified in dermos copy images at an earlier stage. On the other hand, the detection of melanomas is an incredibly challenging task. Consequently, the detection and recognition of skin cancer are of tremendous assistance to the accuracy of pathologists. In this research, a deep learning technique is shown for reliably diagnosing the type of melanoma present at a preliminary phase. The proposed model makes a distinction among lesion maligna, superficial spreading, and nodular melanoma. This permits the early diagnosis of the virus and the quick isolation and therapy necessary to stop the transmission of infection further. Deep learning (DL) and the standard non-parametric machine learning method are exemplified in the deep layer topologies of the convolutional neural network (CNN), which are neural network algorithms. The effectiveness of a CNN classifier was evaluated using data retrieved from the website https://dermnetnz.org/. The outcomes of the experiments show that the proposed method is superior in terms of diagnostic accuracy compared to the methodologies that are currently considered state of the art.

Skin Cancer Recognition Using SVM Image Processing Technique

According to some estimates, skin cancer is the leading cause of death for people in the modern period. Although it can happen anywhere on the body, the majority of the time, this sort of cancer targets particular body regions that are more prone to being exposed to light. Skin cells proliferate in an erratic or patchy manner as a symptom. The majority of skin cancers can be treated if they are discovered early. Therefore, a patient’s life can be saved by promptly and precisely diagnosing skin cancer. Modern technology has made it feasible to detect skin cancer in its earliest stages. Skin cancer can be diagnosed systematically using the biopsy procedure [1]. Skin cells are removed and supplied as a sample, following which they are evaluated in several laboratories. It’s an extremely unpleasant and time-consuming process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on support vector machine (SVM). It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called SVM are used in the identification process. In particular, epiluminescence microscopy is utilized using an image and several preprocessing techniques which are used in the reduction of sound artifacts and improve the quality of images. Segmentation is done by using certain thresholding techniqueslikeOtsu.Thegraylevelco-occurrencematrixtechniquemustbeusedtoremovecertainimagefeatures. These characteristics are fed into the classifier as input. The supervised learning model called SVM is used to distinguish data sets. It determines whether a picture is cancerous or not.

Comparative Study of Single Biological Neuron with an Artificial Neuro

A number of artificial neural models have been presented in the literature in an effort to suggest a more accurate representation of a single biological neuron. There are numerous publications on synthetic neurons that attemptedtoreplicateasinglebiologicalneuron,however,suchmodelswereunabletogeneratethespikingpatterns of a real biological neuron. Therefore, there is still scope to design and research improved spiking neural models that more accurately reflect the functions of a biological neuron. This motivation drives extensive modification of an artificial neuron model to produce the spike patterns of a real biological neuron. The modified single artificial neuronmodel thathasbeen proposedexhibitsthefunctions ofabiological neuron.It’sstill crucialtomodel spiking bio-neuronbehavior.Modelingaspikingbio-neuronisstillanimportantexerciseinviewofpossibleapplicationsof the underlying features in the areas of neuromorphic engineering, cognitive radio, and spiking neural networks.

Selective cytotoxicity mechanisms and biodistribution of diamond nanoparticles on the skin cancer in C57 mouse

The cytotoxicity of diamond nanoparticles (DNs) to various cell lines has been on focus by numerous scientists. The cellular toxicity system of DNs has not been fully understood or explained in skin cancer, at this point. This research was carried out to discover and reveal the potential impacts of DNs on the secluded brain, heart, liver, kidney, and skin in addition to evaluation of their cytotoxicity mechanism under test conditions. Their biological activities, for example cell viability, the level of reactive oxygen species (ROS), lipid peroxidation, cytochrome c release and Apoptosis/Necrosis were evaluated. Additionally, the bio-distribution of these nanomaterials in tissues was examined in the C57 mouse. Relying on the findings of the investigation, DNs were found to increase the ROS level, Malondialdehyde (MDA) content, release of cytochrome c, and cell death in skin significantly compared to other groups. In the C57 mouse, DNs were observed to have accumulated in skin tissue more intensively than they did in other organs. The present study presents for the proof that DNs can completely induce cell death signaling in skin cancer without bringing about a high cytotoxicity in other tissues. Results suggest that DNs can be valuable in recognition of skin cancer.

A comparative analysis of melanoma detection methods based on computer aided diagnose system

Skin cancer is a very common type of cancers. It is increasing day by day. Among all skin cancers melanoma falls in most dangerous category. Melanoma has caused highest mortality rate. It can be cured if it is diagnosed earlier. In 2020 there will be 6850 death due to melanoma. Computational methods are proved to be a backbone for early detection of this deadly disease. It is very hard to differentiate between melanoma and non melanoma lesion due to low contrast and high degree of similarity in visualisation. Computer aided diagnose (CAD), a non invasive technique is seen to improve early and faster identification of melanoma. CAD has four steps: Noiseless Pre processing, trustworthy lesion segmentation, relevant feature extraction and an accurate classifier. This paper presents main and current computational methods for skin cancer recognition with dermoscopic images. This paper also presents statistics and results by comparing the performance of classifiers as well as pre processing, segmentation and feature extraction methods.

Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques.

Simple SummaryThis study aimed to investigate the efficacy of implementation of novel skin surface fractal dimension features as an auxiliary diagnostic method for melanoma recognition. We therefore examined the skin lesion classification accuracy of the kNN-CV algorithm and of the proposed Radial basis function neural network model. We found an increased accuracy of classification when the fractal analysis is added to the classical color distribution analysis. Our results indicate that by using a reliable classifier, more opportunities exist to detect timely cancerous skin lesions.(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases; (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification.

Dermatologic simulations in nurse practitioner education: Improving skin cancer knowledge, confidence, and performance.

The rising incidence of skin cancer continues to be a significant public health concern in the United States. Nurse practitioners (NPs) have a key role in early detection of skin cancer. However, NP students currently demonstrate low levels of knowledge related to skin cancer assessments and recognition of skin lesions. The aim of this pilot study was to determine if simulations with standardized patients wearing three-dimensional (3D) prosthetic skin lesions improved NP students' knowledge, self-confidence, and observed simulation performance with regard to assessing, diagnosing, and managing patients with nonmalignant versus malignant skin lesions. A quasi-experimental approach was used with a convenience sample of 39 NP students. Students participated in three consecutive simulations, each followed by a debriefing. Performance was assessed during the simulations using a rubric. Knowledge and self-confidence were measured immediately before and after the simulation. Correct responses on knowledge items increased for 14 of the 18 items. Self-confidence significantly improved from pretest to posttest. There was a statistically significant improvement in performance from the first to third simulation encounter. Simulation with standardized patients wearing 3D lesions can enhance NP students' knowledge, self-confidence, and performance of skin cancer assessments. Nurse practitioner programs can consider using simulation to prepare students for dermatologic care and to help improve the probability of early detection of skin cancer in various health care settings.

A machine learning approach for the recognition of melanoma skin cancer on macroscopic images

In the last years, computer vision systems for the detection of skin cancer have being proposed, specially using machine learning techniques for the classification of the disease and features based on the ABCD dermatology criterion, which gives information on the status of the skin lesion based on static properties such as geometry, color and texture, making it an appropriate criterion for medical diagnosis systems that work through images. This paper proposes a novel skin cancer classification system that works on images taken from a standard camera and studies the impact on the results of the smoothed bootstrapping, which was used to augment the original dataset. Eight classifiers with different topologies (KNN, ANN and SVM) were compared, with and without data augmentation, showing that the classifier with the highest performance as well as the must balanced one was the ANN with data augmentation, achieving an AUC of 87.1%, which saw an improvement from an AUC of 84.3% of the ANN trained with the original dataset.

Classification of skin cancer images by applying simple evolving connectionist system

<span id="docs-internal-guid-eea5616b-7fff-5d26-eeb4-1d8c084ec93d"><span>Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.</span></span>

An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models

Skin cancer is one of the top three perilous types of cancer caused by damaged DNA that can cause death. This damaged DNA begins cells to grow uncontrollably and nowadays it is getting increased speedily. There exist some researches for the computerized analysis of malignancy in skin lesion images. However, analysis of these images is very challenging having some troublesome factors like light reflections from the skin surface, variations in color illumination, different shapes, and sizes of the lesions. As a result, evidential automatic recognition of skin cancer is valuable to build up the accuracy and proficiency of pathologists in the early stages. In this paper, we propose a deep convolutional neural network (DCNN) model based on deep learning approach for the accurate classification between benign and malignant skin lesions. In preprocessing we firstly, apply filter or kernel to remove noise and artifacts; secondly, normalize the input images and extract features that help for accurate classification; and finally, data augmentation increases the number of images that improves the accuracy of classification rate. To evaluate the performance of our proposed, DCNN model is compared with some transfer learning models such as AlexNet, ResNet, VGG-16, DenseNet, MobileNet, etc. The model is evaluated on the HAM10000 dataset and ultimately we obtained the highest 93.16% of training and 91.93% of testing accuracy respectively. The final outcomes of our proposed DCNN model define it as more reliable and robust when compared with existing transfer learning models.

Artificial intelligence in medicine and dermatology.

As technology advances rapidly and vast amounts of data are collected, artificial intelligence (AI) is increasing its presence in our lives. Medicine is a major focus point of AI developers. There are examples of algorithms on par with medical professionals, the most prominent case being skin cancer recognition. However, advancement involves the necessity to adapt to technology and to patients utilizing it on a daily basis. What is more, patients present growing trust towards machine-aided health care. Dermatology is a potent field for AI use as visual data are easy to collect, hold a lot of information and are paramount for diagnosis.

Periocular skin cancer: Diagnosis and management

The periocular region sees high rates of cancer compared to the rest of the body and can be affected by several unique tumors. Treatment typically involves surgical excision, although adjunctive therapies and prognosis vary depending on the diagnosis. Basal cell carcinoma (BCC) is the most common malignancy affecting this region and should be excised promptly. This is followed by squamous cell carcinoma, which is often mistaken for BCC but is treated similarly. Melanoma is less common but more deadly, with relatively high rates of metastasis and death. Rare tumors that may be found in this region include sebaceous carcinoma, Merkel cell carcinoma, and microcystic adnexal carcinoma. While uncommon, these tumors are associated with poor outcomes and frequent recurrence. Recognition of periocular skin cancer is an important skill for primary care physicians, as prompt diagnosis and treatment can be sight or life-saving.