516 research outputs found
Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet
Skin cancer, a major form of cancer, is a critical public health problem with
123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma
cases worldwide each year. The leading cause of skin cancer is high exposure of
skin cells to UV radiation, which can damage the DNA inside skin cells leading
to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed
visually employing clinical screening, a biopsy, dermoscopic analysis, and
histopathological examination. It has been demonstrated that the dermoscopic
analysis in the hands of inexperienced dermatologists may cause a reduction in
diagnostic accuracy. Early detection and screening of skin cancer have the
potential to reduce mortality and morbidity. Previous studies have shown Deep
Learning ability to perform better than human experts in several visual
recognition tasks. In this paper, we propose an efficient seven-way automated
multi-class skin cancer classification system having performance comparable
with expert dermatologists. We used a pretrained MobileNet model to train over
HAM10000 dataset using transfer learning. The model classifies skin lesion
image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36
percent and top3 accuracy of 95.34 percent. The weighted average of precision,
recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The
model has been deployed as a web application for public use at
(https://saketchaturvedi.github.io). This fast, expansible method holds the
potential for substantial clinical impact, including broadening the scope of
primary care practice and augmenting clinical decision-making for dermatology
specialists.Comment: This is a pre-copyedited version of a contribution published in
Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R.,
Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The
definitive authentication version is available online via
https://doi.org/10.1007/978-981-15-3383-9_1
Analysis of the contour structural irregularity of skin lesions using wavelet decomposition
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
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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