The histopathology images are effective in identifying the location and level of cancer. In this chapter, a novel model is implemented for an automatic classification of histopathological images related to lung tissues. Initially, the color normalization technique is applied for improving the contrast of the histopathological images, which are acquired from the LC25000 lung histopathological image dataset. Additionally, the cancer segmentation is accomplished utilizing saliency driven region edge-based top-down level set (SDREL). Further, the feature descriptors—Alexnet and Gray Level Co-Occurrence Matrix (GLCM) features—were used for extracting the feature vectors from the segmented histopathology images. Lastly, the enhanced grasshopper optimization algorithm (EGOA) and random forest classifier were used for optimal feature selection and lung tissue classification. The simulation result shows that the EGOA-random forest model obtained 98.50% of accuracy on the LC25000 lung histopathological image dataset.
ABSTRACT:The issue of health care assumes prime importance for the society and is a significant indicator of social development. Health is clearly not the mere absence of disease but confers on a person or group"s freedom from illness and the ability to realize one"s potential. Health is therefore best understood as the indispensable basis for defining a person"s sense of well-being. The delivery of health care services thus assumes greater proportion, and in this context the role played by information and communication technology has certainly a greater contribution for its effective delivery mechanism. The application of data mining is specifically relevant and it has been successfully applied in medical needs for its reliable precision accuracy and expeditious beneficial results. The various available application techniques have been discussed and analyzed for the purpose of the paper.
In DNA microarray technology, gene classification is considered to be difficult because the attributes of the data, are characterized by high dimensionality and small sample size. Classification of tissue samples in such high dimensional problems is a complicated task. Furthermore, there is a high redundancy in microarray data and several genes comprise inappropriate information for accurate classification of diseases or phenotypes. Consequently, an efficient classification technique is necessary to retrieve the gene information from the microarray experimental data. In this paper, a classification technique is proposed that classifies the microarray gene expression data well. In the proposed technique, the dimensionality of the gene expression dataset is reduced by Probabilistic PCA. Then, an Artificial Neural Network (ANN) is selected as the supervised classifier and it is enhanced using Evolutionary programming (EP) technique. The enhancement of the classifier is accomplished by optimizing the dimension of the ANN. The enhanced classifier is trained using the Back Propagation (BP) algorithm and so the BP error gets minimized. The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. The proposed technique is evaluated by classification performance over the cancer classes, Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). The classification performance of the enhanced ANN classifier is compared over the existing ANN classifier and SVM classifier.
This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS.
In this chapter, chimp optimization algorithm (ChOA) a bio-inspired optimized technique are proposed for selection of features to increase the classification accuracy of heart disease diagnosis. In this approach, noises contained in the cardiac image are removed using median filter initially. Then, GLCM features are extracted from the cardiac image. Among the extracted features, optimal features are chosen using ChOA algorithm. These selected features are taken as input to the classifier. In this approach, support vector neural network (SVNN) is used as a classifier. The classifier classifies the image into normal and abnormal. Simulation results depict that the ChOA-based SVNN performs better than the conventional SVNN, ANN, KNN, and SVM in terms of accuracy.
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