Methods for iris classification and macro feature detection

This work deals with two distinct aspects of iris-based biometric systems: iris classification and macro-feature detection. Iris classification will benefit identification systems where the query image has to be compared against all identities in the database. By preclassifying the query image based on its texture, this comparison is executed only against those irises that are from the same class as the query image. In the proposed classification method, the normalized iris is tessellated into overlapping rectangular blocks and textural features are extracted from each block. A clustering scheme is used to generate multiple classes of irises based on the extracted features. A minimum distance classifier is then used to assign the query iris to a particular class. The use of multiple blocks with decision level fusion in the classification process is observed to enhance the accuracy of the method.;Most iris-based systems use the global and local texture information of the iris to perform matching. In order to exploit the anatomical structures within the iris during the matching stage, two methods to detect the macro-features of the iris in multi-spectral images are proposed. These macro-features typically correspond to anomalies in pigmentation and structure within the iris. The first method uses the edge-flow technique to localize these features. The second technique uses the SIFT (Scale Invariant Feature Transform) operator to detect discontinuities in the image. Preliminary results show that detection of these macro features is a difficult problem owing to the richness and variability in iris color and texture. Thus a large number of spurious features are detected by both the methods suggesting the need for designing more sophisticated algorithms. However the ability of the SIFT operator to match partial iris images is demonstrated thereby indicating the potential of this scheme to be used for macro-feature detection

The effectiveness of methods and algorithms for detecting and isolating factors that negatively affect the growth of crops

This article discusses a large number of textural features and integral transformations for the analysis of texture-type images. It also discusses the description and analysis of the features of applying existing methods for segmenting texture areas in images and determining the advantages and disadvantages of these methods and the problems that arise in the segmentation of texture areas in images. The purpose of the ongoing research is to use methods and determine the effectiveness of methods for the analysis of aerospace images, which are a combination of textural regions of natural origin and artificial objects. Currently, the automation of the processing of aerospace information, in particular images of the earth’s surface, remains an urgent task. The main goal is to develop models and methods for more efficient use of information technologies for the analysis of multispectral texture-type images in the developed algorithms. The article proposes a comprehensive approach to these issues, that is, the consideration of a large number of textural features by integral transformation to eventually create algorithms and programs applicable to solving a wide class of problems in agriculture.

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Invariant Scattering Transform for Medical Imaging

Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imaging applications such as segmentation, classification, and registration, which can be integrated into machine learning algorithms for disease detection, diagnosis, and treatment planning. Additionally, combining IST with deep learning approaches has the potential to leverage their strengths and enhance medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

Malignant skin melanoma detection using image augmentation by oversampling in nonlinear lower-dimensional embedding manifold

The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in a high mortality rate of the affected patients due to late detection. Some challenges affecting the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labeled data (ground truth), and imbalance of skin cancer datasets. This study presents a novel data augmentation technique based on covariant Synthetic Minority Oversampling Technique (SMOTE) to address the data scarcity and class imbalance problem. We propose an improved data augmentation model for effective detection of melanoma skin cancer. Our method is based on data oversampling in a nonlinear lower-dimensional embedding manifold for creating synthetic melanoma images. The proposed data augmentation technique is used to generate a new skin melanoma dataset using dermoscopic images from the publicly available P H2 dataset. The augmented images were used to train the SqueezeNet deep learning model. The experimental results in binary classification scenario show a significant improvement in detection of melanoma with respect to accuracy (92.18%), sensitivity (80.77%), specificity (95.1%), and F1-score (80.84%). We also improved the multiclass classification results in melanoma detection to 89.2% (sensitivity), 96.2% (specificity) for atypical nevus detection, 65.4% (sensitivity), 72.2% (specificity), and for common nevus detection 66% (sensitivity), 77.2% (specificity). The proposed classification framework outperforms some of the state-of-the-art methods in detecting skin melanoma.publishedVersio