Melanoma Skin Cancer Detection Research Articles

Convolutional Neural Networks Framework for the Early Classification and Detection of Melanoma Skin Cancer

Convolutional Neural Networks Framework for the Early Classification and Detection of Melanoma Skin Cancer

Automatic Melanoma Skin Cancer Detection and Segmentation using Snakecut Algorithm

Early detection of melanoma skin cancer is crucial for effective treatment, and computer-aided diagnostic technologies offer promising advancements for dermatologists to make faster, more precise diagnoses of skin lesions. Segmenting skin lesions is a crucial first step towards automated Computer-Aided Diagnosis for skin cancer. This paper aims to use SnakeCut, a foreground extraction approach, to automatically segment skin lesions in HSV color space with little human interaction. Active contour (otherwise called Snake) and Improved GrabCut are the two popular methods. By decreasing the energy function of the related contour, the active contour acts as a deformable segmentation contour. Improved GrabCut uses updated iterated graph cuts to store color attributes used as segmentation signals in order to achieve foreground segmentation from close-by pixel similarities in its foreground segmentation algorithm. The proposed integrated solution, which is predicated on a probabilistic framework, is termed “SnakeCut.” We removed the outer black border using preprocessing. Later feature extraction is done using HOG and HSV and classifies the benign or melanoma state using Naïve Bayes, Decision tree, and K-nearest neighbor classifiers. The efficiency of the segmentation strategy was measured using the Jaccard Index. We compared the classification results of our method with existing state-of-the-art approaches. The study demonstrates the efficacy of Automatic SnakeCut in accurately segmenting skin lesions, thereby enhancing the performance of subsequent classification tasks. The average F-score was 0.75 on the 2017 ISIC challenge training dataset of 100 images. Compared to other methods, this study’s findings reveal that the suggested method is highly accurate.

Skin Imaging Using Optical Coherence Tomography and Photoacoustic Imaging: A Mini-Review

This article provides an overview of the progress made in skin imaging using two emerging imaging modalities, optical coherence tomography (OCT) and photoacoustic imaging (PAI). Over recent years, these technologies have significantly advanced our understanding of skin structure and function, offering non-invasive and high-resolution insights previously unattainable. The review begins by briefly describing the fundamental principles of how OCT and PAI capture images. It then explores the evolving applications of OCT in dermatology, ranging from diagnosing skin disorders to monitoring treatment responses. This article continues by briefly describing the capabilities of PAI imaging, and how PAI has been used for melanoma and non-melanoma skin cancer detection and characterization, vascular imaging, and more. The third section describes the development of multimodal skin imaging systems that include OCT, PAI, or both modes. A comparative analysis between OCT and PAI is presented, elucidating their respective strengths, limitations, and synergies in the context of skin imaging.

Early detection of melanoma skin cancer: A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional neural network

Skin cancer is a prevalent type of disease that is challenging to predict, and early detection is crucial for successful treatment. In this study, we propose an improved strategy for early detection of three types of skin cancers using medical imaging. Our approach uses fuzzy C-means clustering for image segmentation, along with various filters and image features including Local Binary Pattern (LBP), RGB color-space, and GLCM methods. We also employ a Convolutional neural network (CNN) trained with differential evolution (DE) algorithm for classification. We evaluate the proposed technique using skin cancer image datasets HAM10000, and demonstrate its superior performance compared to traditional classifiers. Our approach achieves a detection accuracy of 91 %, which is significantly higher than other traditional methods in the same domain. To enhance the accuracy of skin cancer detection in medical imaging, the proposed technique can be automated using electronic devices like mobile phones.

Application of Deep Learning with ResNet50 for Early Detection of Melanoma Skin Cancer

Cancer is a type of disease that can be fatal. Some of the cancers with the highest death rates in Indonesia include uterine cancer, breast cancer and skin cancer. The most malignant types of skin cancer are melanoma, which has a high mortality rate, especially if not detected in the early stages, and non-melanoma skin cancer (NMS Cs). Management of this disease depends on whether the type of skin cancer is malignant (malignant) or non-malignant (benign). Therefore, we need a system that can classify types of skin cancer with high accuracy. In this research, the author will use deep learning with the InceptionV3 and ResNet50 algorithms to carry out classification. The aim of this research is to classify types of skin cancer using the InceptionV3 and ResNet50 architecture. The skin cancer dataset used consists of two classes, namely Benign and Malignant, with a total of 3297 data, consisting of 660 data for testing and 2637 data for training. Research stages include data acquisition, preprocessing, classification, and analysis of results. Experimental results show that ResNet-50 produces the best performance with an accuracy level of 0.87. Innovations from this research include using a larger dataset, testing two deep learning architectures, modifying hyperparameters, and using a different layer architecture, which produces better accuracy than previous research. It is hoped that the results of this research can be applied to classify skin cancer more accurately.

Novel Approach with Deep Learning Models For Melanoma Skin Cancer Detection

Novel Approach with Deep Learning Models For Melanoma Skin Cancer Detection

Melanoma Skin Cancer Detection using SVM and CNN

In the field of cancer detection and prevention, doctors and patients are facing numerous challenges when it comes to cancer prediction. Melanoma skin cancer is a deadly type of skin cancer with a multitude of variants spread across the world. Traditional methods involved manual inspection followed by various tests of samples. This time-consuming work and inaccurate predictions sometimes risk the overall health of the patient. The two aspects of solving skin cancer detection problems utilising both conventional image-processing techniques and methods based on machine learning and deep learning are elaborated in this article. It gives a review of current skin cancer detection techniques, weighs the benefits and drawbacks of those techniques, and introduces some relevant cancer datasets. The proposed method focuses mainly on Melanoma skin cancer detection and its previous stages (Common Nevus and Atypical Nevus). The methods being proposed employ a blend of colour, texture, and shape characteristics to derive distinguishing attributes from the images. Using CNN (convolutional neural networks) and SVM (support vector machine) algorithms to identify the type of skin cancer the patient is affected with and achieved an accuracy of 92% and 95% respectively.

Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique

Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio.

Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification

Melanoma is one of the most dangerous types of skin cancer. Since 2018, the number of skin cancer cases in the US has increased and exceeded 100,000. Melanoma is the third most common cancer in Indonesia, following womb cancer and breast cancer. Standard detection of melanoma skin cancer biopsy is costly and time-consuming. The purpose of this research is to build and compare melanoma skin cancer detection using various Convolutional Neural Network method. This research used four CNN model architectures methods, VGG-16, LeNet, Xception, and MobileNet. The dataset for this research is image data that consists of 9605 data divided into benign and malignant classes. The data will be augmented to increase its quantity. After that, the data will be trained using four CNN architecture models and evaluated using the confusion matrix. The result of this study is that Xception model has the best accuracy and the lowest loss, with 93% accuracy and 19% loss, with precision 93%, recall 93,5%, and f1-score 93%. Whereas the other model, VGG-16 gives 90 % accuracy, 27% loss, LeNet 89,7% accuracy, 28% loss, and mobileNet 90,8% accuracy and 22,5% loss.

Ensemble optimization algorithm for the prediction of melanoma skin cancer

One of the worst illnesses in the world, melanoma has the potential to spread to many body sites if it is not detected early. Because of this, the use of automated diagnostic tools that may help doctors and even laypeople identify a certain illness has resulted in a huge advancement in the medical field. Create a hybrid method for analyzing suspicious lesions and melanoma skin cancer detection. The current scores' performances are heavily reliant on fine-tuned settings and architectures. Even in machine learning and computer vision studies, dynamic data augmentation studies are limited. This work proposes dynamic training/testing enhancements that can greatly enhance effectiveness of proposals. Traditional search strategies that require training new models for augmentations consume more GPU time than this work's proposed framework. The EOA (ensemble optimization algorithm), which does not require model training for every new augmentation technique, accelerates search. The effectiveness of this technique is evaluated against single and ensemble models using the ISIC dataset. Moreover, Efficient Net, a new, compact network design, serves as the system's backbone. This approach yields greater results, and this research also reveals the sought augmentation strategy that was used, which calls for an exceptional amount of resources. So, to enhance performance, other researchers may make advantage of the augmentation strategies found.

Classification & Detection of Melanoma Skin Cancer in Early Stages Using CNN

Abstract: Melanoma, sometimes referred to as skin cancer, is one of the most severe forms of skin disease. Applying UV radiation to the skin effects or modifies the melanocytes, leading to uncontrolled cell growth and skin burning or shading. Some skin conditions, however less frequent than others, are still harmful because, if untreated, they will probably worsen and spread. Early on, it might not get the proper care. A biopsy is the standard procedure for finding skin cancer and it entails scraping out the patient's skin lesion and sending it for laboratory testing. This strategy is uncomfortable, difficult, and tiresome. A dermatologist must put a lot of work into treating skin cancer from melanoma. In this study, a novel approach is used to detect skin cancer using machine learning. Dermoscopy images of melanoma cancer make it easier and less tedious to detect skin cancer.

Optimization of Melanoma Skin Cancer Detection with the Convolutional Neural Network

Currently, skin cancer is a very dangerous disease for humans. Skin cancer is classified into many types such as Melanoma, Basal and Squamous cell carcinoma. In all types of cancer, melanoma is the most dangerous and unpredictable disease. Detection of melanoma cancer at an early stage is useful for effective treatment and can be used to classify types of melanoma cancer. New innovations in the classification and detection of skin cancer using artificial neural networks continue to develop to assist the medical and medical world in analyzing images precisely and accurately. The method used in this research is Convolutional Neural Network (CNN) with MobileNet model architecture. Skin cancer detection consists of five important stages, namely image database collection, preprocessing methods, augmentation data, model training and model evaluation. This evaluation was carried out using the MobileNet method with an accuracy of 88%.

Melanoma Skin Cancer Detection using Image Processing

Abstract: Early detection of melanoma skin cancer is crucial for effective treatment. Melanoma is considered the most dangerous form of skin cancer due to its higher likelihood of spreading to other parts of the body if not diagnosed and treated promptly. To address this challenge, non-invasive medical computer vision and medical image processing techniques have become increasingly important in clinical diagnosis of various diseases, including melanoma. These techniques offer automated image analysis tools that enable accurate and rapid evaluation of skin lesions.To assess the severity of the lesions, a total dermo copy score was calculated based on the extracted features. Finally, classification was carried out using a convolutional neural network (CNN) model. The results of the study demonstrated a high classification accuracy of 96.5%, indicating the effectiveness of the proposed approach in accurately identifying melanoma skin cancer.

Classification Optimization of Skin Cancer Using the Adaboost Algorithm

Early detection of melanoma skin cancer is crucial in improving prognosis and saving lives. This research aimed to optimize the classification of melanoma images using the Adaboost algorithm. Employing a dataset of 10,000 melanoma images, the study combined the Canny method for image segmentation, Hu Moments for feature extraction, and the Adaboost algorithm for classification. The 5-fold cross-validation results revealed an average accuracy of 61.52%. While the precision consistently surpassed recall, indicating the model's conservative nature in predicting positive cases. The outcomes align with previous research, emphasizing the challenges in melanoma classification. This study contributes to the domain by showcasing the potential and areas of improvement for machine learning in early melanoma detection. Future research is recommended to explore hybrid models and diversify data sources for enhanced robustness and generalizability.

Skin Cancer Detection using Image Processing

Abstract: Early detection of melanoma skin cancer is crucial for effective treatment. Among various types of skin cancer, melanoma is considered the most dangerous due to its high likelihood of spreading to other parts of the body if not diagnosed and treated promptly. In recent years, non-invasive medical computer vision and medical image processing techniques have gained significant importance in clinical diagnosis. These techniques offer automated image analysis tools that enable accurate and rapid evaluation of skin lesions. The study involves several steps, including the collection of a dermo image database, preprocessing, segmentation using thresholding, and extraction of statistical features such as gray level co-occurrence matrix (GLCM), asymmetry, border color, and diameter. Feature selection is performed using principal component analysis (PCA), followed by the calculation of a total dermo copy score. The final step involves classification using convolutional neural networks (CNN). The results of the study indicate an impressive classification accuracy of 96.5%.

An Educational Initiative for Family Nurse Practitioners to Detect Melanoma Skin Cancer

Background Skin cancer is the most common type of cancer in the United States. Melanoma skin cancer is the deadliest form of skin cancer. In the United States, the rates of melanoma skin cancer continue to rise more than any other type of cancer. Local Problem Nurse practitioners play an important role in early detection of skin cancer; however, minimal time is spent on dermatology training in current nurse practitioner programs to sufficiently prepare nurse practitioners to identify melanoma skin cancer. Early detection of melanoma skin cancer leads to decreased mortality. Methodology There were approximately 75 family nurse practitioners eligible to participate. The potential participants were asked to complete the online demographic survey and a pretest questionnaire. After intervention, the participants were asked to complete a posttest questionnaire. Intervention A web-based educational tool regarding identification of melanoma skin cancer was developed. The family nurse practitioners viewed the web-based educational module regarding melanoma skin cancer. Results A comparison of pretest and posttest means from the questionnaire was analyzed using a t test. Thirty-three family nurse practitioners showed statistically significant improvements in knowledge after the educational intervention. In addition, confidence increased in their ability to detect melanoma skin cancer. Conclusions This project has shown that a web-based educational module can improve family nurse practitioners' ability and confidence to detect melanoma skin cancer. This educational module was statistically effective and provides an educational framework that can be adopted by other healthcare organizations to improve nurse practitioner knowledge surrounding melanoma detection.

Detection and diagnosis of melanoma skin cancers in dermoscopic images using pipelined internal module architecture (PIMA) method.

Detection and diagnosis of melanoma skin cancer is important to save the life of humans. The main objective of this article is to perform both detection and diagnosis of the skin cancers in dermoscopy images. Both skin cancer detection and diagnosis system uses deep learning architectures for the effective performance improvement as the main objective. The detection process involves by identifying the cancer affected skin dermoscopy images and the diagnosis process involves by estimating the severity levels of the segmented cancer regions in skin images. This article proposes parallel CNN architecture for the classification of skin images into either melanoma or healthy. Initially, color map histogram equalization (CMHE) method is proposed in this article to enhance the source skin images and then thick and thin edges are detected from the enhanced skin image using the Fuzzy system. The gray-level co-occurrence matrix (GLCM) and Law's texture features are extracted from the edge detected images and these features are optimized using genetic algorithm (GA) approach. Further, the optimized features are classified by the developed pipelined internal module architecture (PIMA) of deep learning structure. The cancer regions in the classified melanoma skin images are segmented using mathematical morphological process and these segmented cancer regions are diagnosed into either mild or severe using the proposed PIMA structure. The proposed PIMA-based skin cancer classification system is applied and tested on ISIC and HAM 10000 skin image datasets. RESEARCH HIGHLIGHTS: The melanoma skin cancer is detected and classified using dermoscopy images. The skin dermoscopy images are enhanced using color map histogram equalization. GLCM and Law's texture features are extracted from the enhanced skin images. To propose pipelined internal module architecture (PIMA) for the classification of skin images.

Proposing a hybrid technique of feature fusion and convolutional neural network for melanoma skin cancer detection

Skin cancer is among the most common cancer types worldwide. Automatic identification of skin cancer is complicated because of the poor contrast and apparent resemblance between skin and lesions. The rate of human death can be significantly reduced if melanoma skin cancer could be detected quickly using dermoscopy images. This research uses an anisotropic diffusion filtering method on dermoscopy images to remove multiplicative speckle noise. To do this, the fast-bounding box (FBB) method is applied here to segment the skin cancer region. We also employ 2 feature extractors to represent images. The first one is the Hybrid Feature Extractor (HFE), and second one is the convolutional neural network VGG19-based CNN. The HFE combines 3 feature extraction approaches namely, Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF) into a single fused feature vector. The CNN method is also used to extract additional features from test and training datasets. This 2-feature vector is then fused to design the classification model. The proposed method is then employed on 2 datasets namely, ISIC 2017 and the academic torrents dataset. Our proposed method achieves 99.85%, 91.65%, and 95.70% in terms of accuracy, sensitivity, and specificity, respectively, making it more successful than previously proposed machine learning algorithms.

Skin Cancer Detection: A Survey

Due to a lack of awareness of its warning signs and preventative measures, skin cancer—one of the deadliest types of cancer—has seen a significant increase in mortality rates. Therefore, early detection at an early stage is essential to halting the spread of cancer. Although there are other types of skin cancer, melanoma is the most dangerous. However, melanoma patients have a 96% survival rate when detected early with straightforward and cost-effective treatments. The project aims to classify various kinds of skin cancer using image processing and machine learning. Melanoma is a type of skin cancer that can be fatal. If detected early, melanoma skin cancer can be completely treated. Because it directly correlates with death, early melanoma skin cancer detection is critical for patients. In this study, early melanoma skin cancer is detected and categorized using a variety of algorithms, including K-means clustering, neural networks, K-Nearest Neighbour, and Naive Bayes.

Survey on Skin Cancer

Abstract: Due to a lack of awareness of the signs and methods for prevention, skin cancer is one of the most deadly types of cancer, and the death rate has dramatically increased. Therefore, in order to stop the spread of cancer, early identification at an early stage is essential. There are other varieties of skin cancer, but melanoma is the most dangerous one. However, if discovered early, melanoma patients have a 96% survival rate with straightforward and affordable therapies. The goal of the project is to identify and categorize different types of skin cancer using machine learning and image processing techniques. Melanoma skin cancer poses a serious and dangerous risk to people. Due to the direct link between melanoma skin cancer and fatalities, early detection of this disease is crucial for patients. Melanoma skin cancer is fully treatable if caught in its early stages. In this study, early melanoma skin cancer detection and classification are performed utilizing a variety of algorithms, including the K-means clustering method, neural networks, K-Nearest Neighbour, and Navie Bays, etc.