INTEGRATED DIAGNOSING OF SKIN DISEASE DETECTION USING KNN

Today, a wide range of illnesses  affect people of all ages. Skin cancer is shown to be one of the most common problems and it has a serious impact on human life and health. An allergy, a fungal infection, a bacterium, harmful UV rays from sunburn, etc. could be the cause of a number of skin diseases. It is possible to recover if the disease can be diagnosed earlier and more accurately. Currently, Artificial Intelligence (AI) has a significant impact on the medical industry. Skin diseases, also known as Cutaneous diseases, affect nearly two out of every three people. One of the most common medical environments is skin disease, and when compared to other diseases, the visual representation of skin disease is especially important. Dermatological diseases are the most common diseases in the world. Despite its prevalence, its diagnosis is highly complex and requires extensive practical experience. An efficient automated technique for identifying people with skin diseases is critically needed. In this approach, the K-NN model is recommended for detecting various skin diseases at an early stage. The recommended procedure will provide the highest level of accuracy for detecting skin diseases. Finally, the recommended model works more efficiently than other existing models

Depth data improves non-melanoma skin lesion segmentation and diagnosis

Examining surface shape appearance by touching and observing a lesion from different points of view is a part of the clinical process for skin lesion diagnosis. Motivated by this, we hypothesise that surface shape embodies important information that serves to represent lesion identity and status. A new sensor, Dense Stereo Imaging System (DSIS) allows us to capture 1:1 aligned 3D surface data and 2D colour images simultaneously. This thesis investigates whether the extra surface shape appearance information, represented by features derived from the captured 3D data benefits skin lesion analysis, particularly on the tasks of segmentation and classification. In order to validate the contribution of 3D data to lesion identification, we compare the segmentations resulting from various combinations of images cues (e.g., colour, depth and texture) embedded in a region-based level set segmentation method. The experiments indicate that depth is complementary to colour. Adding the 3D information reduces the error rate from 7:8% to 6:6%. For the purpose of evaluating the segmentation results, we propose a novel ground truth estimation approach that incorporates a prior pattern analysis of a set of manual segmentations. The experiments on both synthetic and real data show that this method performs favourably compared to the state of the art approach STAPLE [1] on ground truth estimation. Finally, we explore the usefulness of 3D information to non-melanoma lesion diagnosis by tests on both human and computer based classifications of five lesion types. The results provide evidence for the benefit of the additional 3D information, i.e., adding the 3D-based features gives a significantly improved classification rate of 80:7% compared to only using colour features (75:3%). The three main contributions of the thesis are improved methods for lesion segmentation, non-melanoma lesion classification and lesion boundary ground-truth estimation

Computer Aided Diagnostic Support System for Skin cancer: Review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided

Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. © 2013 Ammara Masood and Adel Ali Al-Jumaily

Detection and Classification Techniques for Skin Lesion Images: A Review

Dermoscopy needs sophisticated and robust systems for successful treatment which would also help reduce the number of biopsies. Computer aided diagnosis of melanoma support clinical decision making which would provide relevant supporting evidence from the prior known cases to the dermatologists and practitioners and also ease the management of clinical data. These systems play an important role of an expert consultant by presenting cases that are not only similar in diagnosis but also similar in appearance and help in early detection and diagnosis of skin diseases. With the advances in technology, new algorithms have also been proposed to develop more efficient CAD systems. This article reviews various techniques that have been proposed for detection and classification of skin lesions

Classification of skin tumours through the analysis of unconstrained images

Skin cancer is the most frequent malignant neoplasm for Caucasian individuals. According to the Skin Cancer Foundation, the incidence of melanoma, the most malignant of skin tumours, and resultant mortality, have increased exponentially during the past 30 years, and continues to grow. [1]. Although often intractable in advanced stages, skin cancer in general and melanoma in particular, if detected in an early stage, can achieve cure ratios of over 95% [1,55]. Early screening of the lesions is, therefore, crucial, if a cure is to be achieved. Most skin lesions classification systems rely on a human expert supported dermatoscopy, which is an enhanced and zoomed photograph of the lesion zone. Nevertheless and although contrary claims exist, as far as is known by the author, classification results are currently rather inaccurate and need to be verified through a laboratory analysis of a piece of the lesion’s tissue. The aim of this research was to design and implement a system that was able to automatically classify skin spots as inoffensive or dangerous, with a small margin of error; if possible, with higher accuracy than the results achieved normally by a human expert and certainly better than any existing automatic system. The system described in this thesis meets these criteria. It is able to capture an unconstrained image of the affected skin area and extract a set of relevant features that may lead to, and be representative of, the four main classification characteristics of skin lesions: Asymmetry; Border; Colour; and Diameter. These relevant features are then evaluated either through a Bayesian statistical process - both a simple k-Nearest Neighbour as well as a Fuzzy k-Nearest Neighbour classifier - a Support Vector Machine and an Artificial Neural Network in order to classify the skin spot as either being a Melanoma or not. The characteristics selected and used through all this work are, to the author’s knowledge, combined in an innovative manner. Rather than simply selecting absolute values from the images characteristics, those numbers were combined into ratios, providing a much greater independence from environment conditions during the process of image capture. Along this work, image gathering became one of the most challenging activities. In fact several of the initially potential sources failed and so, the author had to use all the pictures he could find, namely on the Internet. This limited the test set to 136 images, only. Nevertheless, the process results were excellent. The algorithms developed were implemented into a fully working system which was extensively tested. It gives a correct classification of between 76% and 92% – depending on the percentage of pictures used to train the system. In particular, the system gave no false negatives. This is crucial, since a system which gave false negatives may deter a patient from seeking further treatment with a disastrous outcome. These results are achieved by detecting precise edges for every lesion image, extracting features considered relevant according to the giving different weights to the various extracted features and submitting these values to six classification algorithms – k-Nearest Neighbour, Fuzzy k-Nearest Neighbour, Naïve Bayes, Tree Augmented Naïve Bayes, Support Vector Machine and Multilayer Perceptron - in order to determine the most reliable combined process. Training was carried out in a supervised way – all the lesions were previously classified by an expert on the field before being subject to the scrutiny of the system. The author is convinced that the work presented on this PhD thesis is a valid contribution to the field of skin cancer diagnostics. Albeit its scope is limited – one lesion per image – the results achieved by this arrangement of segmentation, feature extraction and classification algorithms showed this is the right path to achieving a reliable early screening system. If and when, to all these data, values for age, gender and evolution might be used as classification features, the results will, no doubt, become even more accurate, allowing for an improvement in the survival rates of skin cancer patients