Diagnosis Of Melanoma Skin Cancer Research Articles

Evaluation of Diffuse Reflectance Spectroscopy Vegetal Phantoms for Human Pigmented Skin Lesions.

Pigmented skin lesions have increased considerably worldwide in the last years, with melanoma being responsible for 75% of deaths and low survival rates. The development and refining of more efficient non-invasive optical techniques such as diffuse reflectance spectroscopy (DRS) is crucial for the diagnosis of melanoma skin cancer. The development of novel diagnostic approaches requires a sufficient number of test samples. Hence, the similarities between banana brown spots (BBSs) and human skin pigmented lesions (HSPLs) could be exploited by employing the former as an optical phantom for validating these techniques. This work analyses the potential similarity of BBSs to HSPLs of volunteers with different skin phototypes by means of several characteristics, such as symmetry, color RGB tonality, and principal component analysis (PCA) of spectra. The findings demonstrate a notable resemblance between the attributes concerning spectrum, area, and color of HSPLs and BBSs at specific ripening stages. Furthermore, the spectral similarity is increased when a fiber-optic probe with a shorter distance (240 µm) between the source fiber and the detector fiber is utilized, in comparison to a probe with a greater distance (2500 µm) for this parameter. A Monte Carlo simulation of sampling volume was used to clarify spectral similarities.

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.

Unintended Bias in Artificial Intelligence Driven Diagnosis of Melanoma: A Systematic Review

Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone have increasingly been recognized as a problem. Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN) models. CNN models are prone to biased output due to biases in the dataset used to train them. Although the incidence of melanoma is lower in patients with darker skin tone, it is associated with a worse prognosis than in Caucasians, underscoring the need for accurate early diagnosis in these patients. Our objective in this systematic review was to assess to what degree race/ethnicity, specifically Black/ African American patient cohort were included in training datasets used in generating machine learning algorithms for automated melanoma diagnosis. Our review documents the fact that there is a remarkable lack of and inconsistent reporting of patient demographics especially race/ethnicity with notable under-representation of patients of color, highlighting a currently unmet critical need of lack of diversity in the publicly available skin image datasets. These publicly available skin image datasets, are an inherently unbalanced unintentionally biased datasets from which AI models created for the diagnosis of melanoma skin cancer have restricted applicability to real life clinical scenarios and limited population representation preventing generalizability to the community as a whole.

Automated differentiation of skin melanocytes from keratinocytes in high‐resolution histopathology images using a weakly‐supervised deep‐learning framework

AbstractEarly detection and accurate diagnosis of melanoma skin cancer which accounts for 75% of all skin cancer mortalities would significantly improve survival rates. Melanoma is characterized by an abnormal proliferation of malignant melanocytes in the epidermis of the skin, which is composed predominantly of keratinocytes. Localization and differentiation of melanocytes from keratinocytes in the skin in whole slide images (WSIs) is an important initial task towards the diagnosis of the tumor. Because of the small size of melanocytes and the dominance of keratinocytes in the epidermis, identification of melanocytes can be challenging and prone to errors. We propose a new fully‐automated framework based on deep‐machine learning to identify melanocytes and keratinocytes with detection accuracy of 90.5% and 87.4%, respectively. This framework begins with segmenting the epidermis layer inside 640 × 320 × 3 super tiles of WSIs using a DeepLabV3+ model followed by a weakly‐supervised deep learning approach deploying a fine‐tuned pre‐trained visual geometry group (VGG)‐16 model, gradient‐weighted class activation mapping (Grad‐CAM), Otsu's and contour estimation methods in order to identify melanocytes and keratinocytes inside each 64 × 64 × 3 tile (i.e., a subdivision of the super tile). The proposed framework outperforms the state‐of‐the‐art methods as well as provides a significant improvement of 28% and 17% over the fully supervised faster region‐based convolutional neural network (R‐CNN) in detecting melanocytes and keratinocytes respectively without the need for expensive expert fine labels for model training and validation. The proposed framework offers a promising accurate tool to aid pathologists in differentiating melanocytes from keratinocytes that would eventually support the diagnosis of melanoma.

A Non-Invasive Interpretable Diagnosis of Melanoma Skin Cancer Using Deep Learning and Ensemble Stacking of Machine Learning Models.

A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in the detection of skin lesions. Still, due to image occlusions and imbalanced datasets, the accuracies have been compromised so far. In this paper, we introduce an interpretable method for the non-invasive diagnosis of melanoma skin cancer using deep learning and ensemble stacking of machine learning models. The dataset used to train the classifier models contains balanced images of benign and malignant skin moles. Hand-crafted features are used to train the base models (logistic regression, SVM, random forest, KNN, and gradient boosting machine) of machine learning. The prediction of these base models was used to train level one model stacking using cross-validation on the training set. Deep learning models (MobileNet, Xception, ResNet50, ResNet50V2, and DenseNet121) were used for transfer learning, and were already pre-trained on ImageNet data. The classifier was evaluated for each model. The deep learning models were then ensembled with different combinations of models and assessed. Furthermore, shapely adaptive explanations are used to construct an interpretability approach that generates heatmaps to identify the parts of an image that are most suggestive of the illness. This allows dermatologists to understand the results of our model in a way that makes sense to them. For evaluation, we calculated the accuracy, F1-score, Cohen’s kappa, confusion matrix, and ROC curves and identified the best model for classifying skin lesions.

Use of Radiation Circuits for Diagnosis of Melanoma Skin Cancer in Images of Skin Lesions Using Convolutional Neural Networks

Melanoma skin cancer is a fatal illness. However, most melanomas can be treated with minimal surgery if found early. In this regard, the addition of image analysis techniques that automate skin cancer diagnosis would support and increase dermatologists’ diagnosis accuracy. As a result, enhanced melanoma detection can benefit patients who are showing indicators of the disease. Convolutional neural networks can learn from features hierarchically. Since the implementation of a neural network requires a large volume of images to achieve high accuracy rates, an insufficient number of skin cancer images represent an additional challenge in the detection of skin lesions; the current work aims to develop an intelligent system that allows, based on the analysis of images of skin lesions and contextual information of the patient, to accurately determine if it represents a case of melanoma‐type skin cancer. The TensorFlow library was used to execute models in the constructed app. The Mobilenet V2 model was used with a collection of 305 pictures retrieved from the Internet. Diagnoses included melanoma, plaque and psoriatic skin conditions, and Kaposi’s sarcoma and atopic dermatitis. There were two separate machines used to conduct the application tests. There was more than 75% acceptable performance in predicting Kaposi’s sarcoma‐like illnesses for melanoma‐like lesions, as well as plaque psoriasis and atopic dermatitis, respectively. Despite the low amount of images used in training, the constructed mobile application performed well.

Automated Skin Lesion Preprocessing Model of Dermoscopic Images Towards Melanoma Detection

Melanoma is lethal form of skin cancer. For unbiased diagnosis of melanoma skin cancer an automated and computerized image analysis system is prime requirement. Clinicians examine the digital photographs taken from dermoscopy lens to detect skin cancer. The images captured usually contains various noise artifacts like low contrast hair objects, ink markers, vignette effect, ebony frames. These artifacts often affect results of skin lesion segmentation and lesion classification analysis. In this paper, we propose an automated image pre-processing model to remove noise artifacts like thin & thick hair objects, surgical ink markers, dark vignette effect, ebony frames from the images. To segment required lesion region and detect lesion edges we applied Otsu thresholding and Canny edge technique. We also applied these techniques on preprocessed image and original image with noise artifacts to check the efficiency of our model. We discuss various methodologies and results acquired at each phase. Based on the experimental results, the proposed model can be used successfully in removal of noise artifacts from dermoscopic images. In future, it can be used to build a reliable and accurate computer aided diagnostic systems.

Understanding the impact of sex and stage differences on melanoma cancer patient survival: a SEER-based study

BackgroundThis 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.MethodsFlexible 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.ResultsAt 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.ConclusionsThis 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.

PCN170 DESPITE INCREASES IN RATE OF HOSPITALIZATION, IN-HOSPITAL MORTALITY AND ADMISSION COSTS ASSOCIATED WITH MELANOMA OF THE SKIN HAVE REMAINED STABLE FROM 2012-2016

Melanoma of the skin represents 5.5% of all new cancer cases in the United States (US). While incidence continues to increase, overall mortality rates have been steadily declining. We investigated the hospitalization and in-hospital mortality trends of melanoma skin cancer in the US over the most recent period of available data. Using 2012-2016 data from the National Inpatient Sample, admissions with a diagnosis of melanoma of the skin were identified. Descriptive measures including demographics, length of stay (LOS), discharge disposition, and total cost (adjusted for inflation using medical care component), stratified by year. Discharge-level weights applied to represent national estimates, and domain analysis used for subpopulation estimates. Annual percentage change calculated to characterize the trend in hospitalization rates over time. In 2016, there were 142,050 admissions of patients with a diagnosis of melanoma skin cancer. Mean (95% confidence interval) age was 70.2 (69.6-70.5) years. Patients were predominantly White (90.8% [89.5%-92.0%]) and covered by Medicare (68.3% [67.4%-69.3%]). Mean age increased each year from 68.9 (68.4-69.3) in 2012 (p<0.001). Hospitalization rates increased from 37 per 100,000 people in 2012 to 44 per 100,000 people in 2016, with an average annual increase of 5.0% (3.2%–6.9%; p=0.003). Mean LOS remained relatively consistent each year from 4.6 (4.5-4.7) in 2012 to 4.6 (4.6-4.7) in 2016 (p=0.053). In-hospital mortality occurred in 2.6% (2.4%-2.8%) of admissions in 2012 and 2.7% (2.6 %-2.9%) in 2016 (p=0.856). The mean cost of admission remained consistent with only a slight decrease from 2012 ($14,473 [$13,975-$14,972]) to 2016 ($14,041 [$13,613-$14,469]) (p=0.782). While the admission rates of melanomas of the skin increased significantly from 2012-2016, hospital mortality rates and costs remained stable in the US. Melanoma skin cancer mortality dropped 2.5% per year over a similar period, suggesting earlier diagnosis and detection, and improved treatment in the outpatient setting.

Automated Non-invasive Diagnosis of Melanoma Skin Cancer using Dermo-scopic Images

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.