Land Cover/Land Use Mapping Using Soft Computing Techniques with Optimized Features

The chapter discusses soft computing techniques for solving complex computational tasks. It highlights some of the soft computing techniques like fuzzy logic, genetic algorithm, artificial neural network, and machine learning. The classification of the remotely sensed images is always a tedious task. So, here we explain how these soft computing techniques could be used for image classification. Image classification mainly concentrates on the feature’s extraction process. The features extracted in an efficient manner improve classification accuracy. Hence, the different kinds of features and different methods for these extractions are explained. The best extracted features are selected using genetic algorithm. Various algorithms are shown and comparisons are made. Finally, the results are verified using a hypothetical case study

Skin Cancer Detection and Classification

Skin cancer is a term given to the uncontrolled growth of strange skin cells. It occurs whenever unrepaired DNA damages to skin cells trigger mutations, or any other genetic defects, that lead the skin cells to multiply readily and form malignant tumors. Image processing is a commonly used method for skin cancer detection from the appearance of the affected area on the skin. The input to the system is that the skin lesion image so by applying novel image process techniques, it analyses it to conclude about the presence of skin cancer. The Lesion Image analysis tools checks for the various Melanoma parameters Like Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by texture, size and form analysis for image segmentation and have stages. The extracted feature parameters are accustomed classify the image as traditional skin and malignant melanoma cancerlesion. Artificial Neural Network (ANN) is one of the important branches of Artificial Intelligence, which has been accepted as a brand-new technology in computer science for image processing. Neural Networks is currently the area of interest in medicine, particularly in the fields of radiology, urology, cardiology, oncology, etc. Neural Network plays a vital role in an exceedingly call network. It has been used to analyze Melanoma parameters Like Asymmetry, Border, Colour, Diameter, etc. which are calculated using MATLAB from skin cancer images intending to developing diagnostic algorithms that might improve triage practices in the emergency department. Using the ABCD rules for melanoma skin cancer, we use ANN in the classification stage. Initially, we train the network with known target values. The network is well trained with 96.9% accuracy, and then the unknown values are tested for the cancer classification. This classification method proves to be more efficient for skin cancer classification

Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification

Classification of MRI Brain images using GLCM, Neural Network, Fuzzy Logic & Genetic Algorithm

Detection of Brain abnormality could be a vital and crucial task in medical field. Resonance Imaging Brain image detection method offers the knowledge of the various abnormalities in Brain. This helps the doctors in treatment coming up with. Within the previous work, within the field of medical image process several scientist and soft computing techniques have totally different strategies like totally automatic and semiautomatic. During this projected technique, 2 totally different classification strategies are used along for the classification of magnetic resonance imaging Brain pictures. Those classification strategies square measure Neural Network and fuzzy logic. With this projected hybrid technique Genetic algorithmic program is employed for the optimization. Projected technique consists of various stages. Knowledge assortment through numerous hospitals or repository sites and convert original data pictures into gray scale image. Gray Level Co-occurrence Matrix technique is employed for the extraction of the options from the gray scale image. Optimization technique Genetic algorithmic program is especially used for reducing the options that square measure extracted by GLCM for simple classification and reducing the convergence time or computation time. there\'s a hybrid classifier is employed for classification of magnetic resonance imaging brain pictures specifically Neural and Fuzzy classifier. DOI: 10.17762/ijritcc2321-8169.15060

A Genetic Programming Approach to Designing Convolutional Neural Network Architectures

The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our method is available at https://github.com/sg-nm/cgp-cn

Image Content Analysis Using Neural Networks and Genetic Algorithms

The analysis of digital images for content discovery is a process of identifying and classifying patterns and sub-images that can lead to recognizing contents of the processed image. The image content analysis system presented in this paper aims to provide the machine with the capability to simulate in some sense, a similar capability in human beings. The developed system consists of three levels. In the low level, image clustering is performed to extract features of the input data and to reduce dimensionality of the feature space. Classification of the scene images are carried out using a single layer neural network, trained through Kohonen's self-organizing algorithm, with conscience function, to produce a set of equi-probable weights vector. The intermediate level consists of two parts. In the first part an image is partitioned into homogeneous regions with respect to the connectivity property between pixels, which is an important concept used in establishing boundaries of objects and component regions in an image. For each component, connected components can be determined by a process of component labeling. In the second part, feature extraction process is performed to capture significant properties of objects present in the image. In the high level; extracted features and relations of each region in the image are matched against the stored object models using the genetic algorithm approach. The implemented system is used in the analysis and recognition of colored images that represent natural scenes. Keywords: genetic algorithms, neural networks, image segmentation, clustering, image content analysis