2,516 research outputs found

    A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

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    Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

    A Mobile-Based Skin Disease Identification System Using Convolutional Neural Networks

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    Skin diseases pose significant challenges in the field of dermatology. In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image recognition and analysis tasks. This research paper presents a comprehensive study on the application of CNNs for skin disease diagnosis. We propose a CNN-based framework for skin disease diagnosis, which utilizes a large dataset of dermatological images to accurately identify various skin diseases. The proposed model leverages the deep learning capabilities of CNNs to learn discriminative features from input images, enabling accurate and efficient diagnosis. We demonstrate improved accuracy and efficiency in skin disease diagnosis by employing pre-trained models. Our proposed model enables accurate classification of skin diseases into high, medium, and low severity categories by leveraging a large dataset of annotated images, assisting healthcare professionals in prioritizing treatment strategies. In conclusion, this research paper presents a comprehensive study on the application of CNNs for skin disease diagnosis, skin lesion classification, melanoma skin cancer classification, and skin disease severity classification. The proposed models showcase significant advancements in the field of dermatology, providing accurate and efficient tools for dermatologists and healthcare professionals. The findings of this research contribute to improving the diagnosis, classification, and severity assessment of skin diseases, ultimately enhancing patient care and treatment outcomes

    Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques

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    More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models

    Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

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    The boundary irregularity of skin lesions is of clinical significance for the early detection of malignant melanomas and to distinguish them from other lesions such as benign moles. The structural components of the contour are of particular importance. To extract the structure from the contour, wavelet decomposition was used as these components tend to locate in the lower frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to give position and frequency resolution invariance. Energy distributions among different wavelet sub-bands were then analyzed to extract those with significant levels and differences to enable maximum discrimination. Based on the coefficients in the significant sub-bands, structural components from the original contours were modeled, and a set of statistical and geometric irregularity descriptors researched that were applied at each of the significant sub-bands. The effectiveness of the descriptors was measured using the Hausdorff distance between sets of data from melanoma and mole contours. The best descriptor outputs were input to a back projection neural network to construct a combined classifier system. Experimental results showed that thirteen features from four sub-bands produced the best discrimination between sets of melanomas and moles, and that a small training set of nine melanomas and nine moles was optimum

    Deep learning and localized features fusion for medical image classification

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    Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible. This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical situations. For local feature development, a new approach based on combining the rising deep learning paradigm with the use of handcrafted features is developed to classify cervical tissue histology images into different cervical intra-epithelial neoplasia classes. Using deep learning combined with handcrafted features improved the accuracy by 8.4% achieving 80.72% exact class classification accuracy compared to 72.29% when using the benchmark feature-based classification method --Abstract, page iv
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