Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

A hierarchical three-step superpixels and deep learning framework for skin lesion classification.

Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

Any superficial skin growth that does not resemble the surrounding area is referred to as skin lesion. It can occur in the form of mole, bump, cyst, rash or other changes that can be classified either as primary or secondary lesion. While primary skin lesions correspond to those changes in color or texture, secondary lesions occur as a primary lesion progression. Skin lesion image segmentation and classification at the early stages can help the patients recover through proper medication and treatment. Many algorithms for segmentation and classification are available in the literature but they all fail to extract lesion boundaries perfectly and classify them with more accuracy. To improve the reliability of the skin image segmentation and classification, we propose to use decision trees and random forest algorithms in this works and compare them with different data sets. The proposed method can generate high-resolution feature maps that can help to preserve the spatial details of the image. While tested against the ISIC 2017 and HAM10000 dataset, we found that the proposed method is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images.

Skin lesion classification using decision trees and random forest algorithms

Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.

Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features

Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier

Spine canal segmentation is an emerging zone in research proposed to help interpretation and processing of advanced MRI and CT images. For instance, high resolution three-dimensional volumes can be divided to provide an estimation of spine canal atrophy. Spine canal segmentation is complex because of assortment of MRI contrasts and variation in human life structures. This investigation illustrates the details of spine canal segmentation techniques and gives a few measurements that can be utilized to contrast with other segmentation strategies. The details of background and foreground subtraction techniques, spine canal segmentation approach and optimization approach which are utilized in the different applications have been considered. In this paper, spine canal segmentation on probabilistic booting tree (PBT) with fuzzy support vector machine performance measures and metrics are analysed in state-of-the art technologies. Proposed approach is performed on the base of the automatic spine canal segmentation with the group of data MR. This proposed segmentation continue with fuzzy support vector machine (FSVM) technique to make fully automatic stream pipeline. The declaration in an automatic segmentation of stream pipeline was implemented with flexible voxel wise classification accompanying dimensions analogous with 3D Haar and labelled machine learning algorithms i.e. probabilistic boosting tree combined fuzzy support vector machine (PBT-FSVM). The novel segmentation technique correlated with MR data sets provides better accuracy than the exiting techniques and it is shown in experimental outcomes. To still improve performance of the results, online learning classification method can be in the proposed work.

An improved approach for automatic spine canal segmentation using probabilistic boosting tree (PBT) with fuzzy support vector machine

Efficient fuzzy based K-nearest neighbour technique for web services classification

The mobile users have acquired the benefits of cloud computing with the help of Mobile Edge Computing (MEC) technology in order to satisfy the increasing data demands. The efficiency of the system is highly limited by the bandwidth limitations and limitations associated with the mobile devices despite the rapid development of MEC as well as the cloud computing technology. Our aim is to provide an optimal method to optimize the energy consumption in the mobile edge computing. In this regard, the research paper proposed a Green Cloud based Queue Management system for 5G networks that helps in addressing the issues related to latency and energy consumption. While serving the users, the proposed methodology results in less amount of energy being wasted and hence the reduced latency. By means of alleviating the congestion and implementing the virtual list, this issue can be resolved greatly. Simulation is done with the help of NS2 green cloud simulator and the results are obtained by comparing the proposed model to conventional cloud model and cloudlet based on throughput, latency, energy consumption and normalized overhead as these are the evaluation measures. The results show that there has been considerable enhancement in the energy consumption. As the throughput increases, the quality of the service also increases.

S. T. Suganthi

Papers

Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier

An improved approach for automatic spine canal segmentation using probabilistic boosting tree (PBT) with fuzzy support vector machine

Efficient fuzzy based K-nearest neighbour technique for web services classification

An Enhanced Green Cloud Based Queue Management (GCQM) System to Optimize Energy Consumption in Mobile Edge Computing