2,825 research outputs found
Kernel Methods for Melanoma Recognition
Malignant melanoma is the most deadly for of skin lesion. Early diagnosis is of critical importance to patient survival. Visual recognition algorithms could potentially be of great help for physicians in a computer assisted diagnosis system. Previous work on this topic has focused mostly on developing ad-hoc segmentation and feature extraction methods. In this paper we take a completely different approach. We put the emphasis on the learning part of the algorithm by using two kernel-based classifiers, one discriminative and one probabilistic. As a discriminative approach we chose support vector machines, a state of the art large-margin classifier which was proved very successful on visual applications. As a probabilistic approach we chose spin glass-Markov random fields, a kernel Gibbs distribution inspired by results of statistical physics. We benchmarked these algorithms against the (to our knowledge) state-of-the-art method on melanoma recognition, exploring how performance changes by using color or textural features, and how it is affected by the quality of the segmentation mask. We show with extensive experiments that the support vector machine approach outperforms the existing method and, on two classes out of three, it achieves performances comparable to those obtained by expert clinician
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
There has been a steady increase in the incidence of skin cancer worldwide,
with a high rate of mortality. Early detection and segmentation of skin lesions
are crucial for timely diagnosis and treatment, necessary to improve the
survival rate of patients. However, skin lesion segmentation is a challenging
task due to the low contrast of lesions and their high similarity in terms of
appearance, to healthy tissue. This underlines the need for an accurate and
automatic approach for skin lesion segmentation. To tackle this issue, we
propose a convolutional neural network (CNN) called SkinNet. The proposed CNN
is a modified version of U-Net. We compared the performance of our approach
with other state-of-the-art techniques, using the ISBI 2017 challenge dataset.
Our approach outperformed the others in terms of the Dice coefficient, Jaccard
index and sensitivity, evaluated on the held-out challenge test data set,
across 5-fold cross validation experiments. SkinNet achieved an average value
of 85.10, 76.67 and 93.0%, for the DC, JI, and SE, respectively.Comment: 2 pages, submitted to NSS/MIC 201
Measles Rash Identification Using Residual Deep Convolutional Neural Network
Measles is extremely contagious and is one of the leading causes of
vaccine-preventable illness and death in developing countries, claiming more
than 100,000 lives each year. Measles was declared eliminated in the US in 2000
due to decades of successful vaccination for the measles. As a result, an
increasing number of US healthcare professionals and the public have never seen
the disease. Unfortunately, the Measles resurged in the US in 2019 with 1,282
confirmed cases. To assist in diagnosing measles, we collected more than 1300
images of a variety of skin conditions, with which we employed residual deep
convolutional neural network to distinguish measles rash from other skin
conditions, in an aim to create a phone application in the future. On our image
dataset, our model reaches a classification accuracy of 95.2%, sensitivity of
81.7%, and specificity of 97.1%, indicating the model is effective in
facilitating an accurate detection of measles to help contain measles
outbreaks
Melanoma Recognition using Kernel Classifiers
Melanoma is the most deadly skin cancer. Early diagnosis is a current challenge for clinicians. Current algorithms for skin lesions classification focus mostly on segmentation and feature extraction. This paper instead puts the emphasis on the learning process, proposing two kernel-based classifiers: support vector machines, and spin glass-Markov random fields. We benchmarked these algorithms against a state-of-the-art method on melanoma recognition. We show with extensive experiments that the support vector machine approach outperforms the other methods, proving to be an effective classification algorithm for computer assisted diagnosis of melanoma
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
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