Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images

Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret’s diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified

Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma

Melanoma is the most widely occurring and life threatening form of skin cancer. Early detection of in situ melanoma has challenged researchers for many decades now. Currently there exists no computer aided mechanisms to accurately detect early melanoma. T he currently existing computer aided diagnostics mechanisms are capable of melanoma classification and are unable to detect in situ melanoma. This paper introduces a Multi Parameter Extraction and Classification System ( 푀푀푀푀푀 ) to aid early detection o f skin cancer melanoma. The 푀푀푀푀푀 defines the skin lesion images in terms of characteristic parameters which are further used for classification. In this paper the extraction of 21 parameters is achieved using a six phase approach. The parameters extr acted are analyzed using statistical methods. It is clear from the results obtained that no single parameter can affirm the detection of in situ melanoma, hence an advanced analysis mechanisms considering all the parameters need to be adopted to effective ly detect melanoma in its initial stages

Skin cancer detection by oblique-incidence diffuse reflectance spectroscopy

Skin cancer is the most common form of cancer and it is on the rise. If skin cancer is diagnosed early enough, the survival rate is close to 90%. Oblique-incidence diffuse reflectance (OIR) spectroscopy offers a technology that may be used in the clinic to aid physicians in diagnosing both melanoma and non-melanoma skin cancers. The system includes a halogen light source, a fiber optic probe, an imaging spectrograph, a charge coupled device (CCD) camera, and a computer. Light is delivered to the skin surface via optical fibers in the probe. After interacting with the skin, the light is collected and sent to the spectrograph that generates optical spectra. Images and histopathological diagnoses were obtained from 250 lesions at the University of Texas M.D. Anderson Cancer Center (Melanoma and Skin Center). To classify OIR data, an image processing algorithm was developed and evaluated for both pigmented and non-pigmented lesions. The continuous wavelet transform and the genetic algorithm were employed to extract optimal classification features. Bayes decision rule was used to categorize spatiospectral images based on the selected classification features. The overall classification accuracy for pigmented melanomas and severely dysplastic nevi is 100%. The overall classification accuracy for non-pigmented skin cancers and severely dysplastic nevi is 93.33%. Oblique-incidence diffuse reflectance spectroscopy and the developed algorithms have high classification rates and may prove useful in the clinic as the process is fast, noninvasive and accurate

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

Análise e comparação do desempenho de diferentes arquiteturas de redes neurais artificiais profundas aplicadas á triagem de lesões de pele

Este trabalho tem como proposta estudar o desempenho de diferentes arquiteturas de redes neurais profundas aplicadas na detecção de lesões dermatológicas. O resultado tem como aplicações secundárias a indicação do encaminhamento correto do paciente e prioridade de atendimento, aumentando assim a eficiência do fluxo de atendimento de pacientes carentes de acesso a essa especialidade médica. O estudo apresenta uma breve introdução dos principais motivos, a origem histórica, o impacto da falta de acesso da população ao sistema de saúde e os potenciais benefícios provenientes da inserção de inteligência artificial nos processos de atendimento. Apresenta ainda uma revisão de trabalhos científicos voltados à classificação de lesões de pele por algoritmos de inteligência artificial e os principais conceitos e histórico de redes neurais artificiais, desde estudos pioneiros até as arquiteturas mais recentes. Como resultados, o trabalho apresenta dois experimentos que somados contemplam diferentes cenários de aplicação das redes neurais profundas para mensurar a acurácia dos algoritmos propostos em classificação de lesões de pele. Os experimentos avaliam o desempenho de diferentes arquiteturas, variando parâmetros e estratégias de inicialização de pesos das redes neurais. Como principais resultados, o trabalho apresenta que a arquitetura GoogLeNet treinada com 24.000 imagens, ao longo de mil épocas utilizando inicialização randômica dos pesos e taxa de aprendizado de - , foi capaz de obter uma acurácia de % para sugestão diagnóstica, % para indicação de prioridade e 96,03% para o encaminhamento apropriado do paciente em um conjunto de teste composto por 6.975 imagens.This work aims to study the performance of several deep neural network architectures applied to classify dermatological lesions. The result has as secondary applications to set the referral and priority of care to the patient, increasing the efficiency to deliver the appropriate treatment for patients lacking access to this medical specialty. The study presents a brief introduction of the main reasons, the historical origin and the impact from the lack of access of the population to the health system. The work also provides the potential benefits from the insertion of artificial intelligence in the health care processes. It also presents a review of scientific studies aimed at the classification of skin lesions by artificial intelligence algorithms and the main concepts and history of artificial neural networks from the beginning to the detailed description of the most recent architectures. As results, the work presents the results of two experiments that together contemplate different scenarios of the application of deep neural networks to measure the accuracy of the proposed algorithms applied to classify skin lesions. The experiments evaluate the performance several architectures, varying parameters and weight initialization strategies. As a main result, the work presents that the GoogLeNet architecture trained with 24,000 images, during a thousand epochs using random initialization as strategy to initialize weights and learning rate of - , was able to obtain an accuracy of 89.72% for diagnostic suggestion, 92 % for priority indication and 96.03% for patient referral in a 6,975 image test set

Genetic Programming based Feature Manipulation for Skin Cancer Image Classification

Skin image classification involves the development of computational methods for solving problems such as cancer detection in lesion images, and their use for biomedical research and clinical care. Such methods aim at extracting relevant information or knowledge from skin images that can significantly assist in the early detection of disease. Skin images are enormous, and come with various artifacts that hinder effective feature extraction leading to inaccurate classification. Feature selection and feature construction can significantly reduce the amount of data while improving classification performance by selecting prominent features and constructing high-level features. Existing approaches mostly rely on expert intervention and follow multiple stages for pre-processing, feature extraction, and classification, which decreases the reliability, and increases the computational complexity. Since good generalization accuracy is not always the primary objective, clinicians are also interested in analyzing specific features such as pigment network, streaks, and blobs responsible for developing the disease; interpretable methods are favored. In Evolutionary Computation, Genetic Programming (GP) can automatically evolve an interpretable model and address the curse of dimensionality (through feature selection and construction). GP has been successfully applied to many areas, but its potential for feature selection, feature construction, and classification in skin images has not been thoroughly investigated. The overall goal of this thesis is to develop a new GP approach to skin image classification by utilizing GP to evolve programs that are capable of automatically selecting prominent image features, constructing new high level features, interpreting useful image features which can help dermatologist to diagnose a type of cancer, and are robust to processing skin images captured from specialized instruments and standard cameras. This thesis focuses on utilizing a wide range of texture, color, frequency-based, local, and global image properties at the terminal nodes of GP to classify skin cancer images from multiple modalities effectively. This thesis develops new two-stage GP methods using embedded and wrapper feature selection and construction approaches to automatically generating a feature vector of selected and constructed features for classification. The results show that wrapper approach outperforms the embedded approach, the existing baseline GP and other machine learning methods, but the embedded approach is faster than the wrapper approach. This thesis develops a multi-tree GP based embedded feature selection approach for melanoma detection using domain specific and domain independent features. It explores suitable crossover and mutation operators to evolve GP classifiers effectively and further extends this approach using a weighted fitness function. The results show that these multi-tree approaches outperformed single tree GP and other classification methods. They identify that a specific feature extraction method extracts most suitable features for particular images taken from a specific optical instrument. This thesis develops the first GP method utilizing frequency-based wavelet features, where the wrapper based feature selection and construction methods automatically evolve useful constructed features to improve the classification performance. The results show the evidence of successful feature construction by significantly outperforming existing GP approaches, state-of-the-art CNN, and other classification methods. This thesis develops a GP approach to multiple feature construction for ensemble learning in classification. The results show that the ensemble method outperformed existing GP approaches, state-of-the-art skin image classification, and commonly used ensemble methods. Further analysis of the evolved constructed features identified important image features that can potentially help the dermatologist identify further medical procedures in real-world situations