CLASSIFICATION OF MEDICAL IMAGES USING MACHINE LEARNING

The popularity of the use of computational tools such as artificial intelligence has been increasing in recent years, and its importance in medicine is a fact. This field has benefited greatly thanks to the incorporation of more effective and faster methodologies in the medical diagnosis and registration processes. In the present work, the classification of images related to three diseases: Tuberculosis, Glaucoma and Parkinson's is carried out. We used deep learning and the RESNET50 convolutional neural network to extract classification characteristics, and then perform the classification based on standard methods, such as support vector machines, Naïve Bayes, and Centroid-based classifier, which are incorporated into two scenarios (cross validation; training and test sets). The classifier's performance is evaluated quantitatively using three evaluation metrics. The results obtained support the feasibility of the proposed methodology and its potential to improve medical diagnosis.

Deep learning for historical books: classification of printing technology for digitized images

Printing technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized collections for this task. To overcome them and to achieve good performance, shallow networks and appropriate sampling strategies needed to be combined. We also show how class activation maps (CAM) can be used to analyze the results.

Deep Learning Approach at the Edge to Detect Iron Ore Type

There is a constant risk of iron ore collapsing during its transfer between processing stages in beneficiation plants. Existing instrumentation is not only expensive but also complex and challenging to maintain. In this research, we propose using edge artificial intelligence for early detection of landslide risk based on images of iron ore transported on conveyor belts. During this work, we defined the device edge and the deep neural network model. Then, we built a prototype will to collect images that will be used for training the model. This model will be compressed for use in the device edge. This same prototype will be used for field tests of the model under operational conditions. In building the prototype, a real-time clock was used to ensure the synchronization of image records with the plant’s process information, ensuring the correct classification of images by the process specialist. The results obtained in the field tests of the prototype with an accuracy of 91% and a recall of 96% indicate the feasibility of using deep learning at the edge to detect the type of iron ore and prevent its risk of avalanche.

Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks

A fire is an extraordinary event that can damage property and have a notable effect on people’s lives. However, the early detection of smoke and fire has been identified as a challenge in many recent studies. Therefore, different solutions have been proposed to approach the timely detection of fire events and avoid human casualties. As a solution, we used an affordable visual detection system. This method is possibly effective because early fire detection is recognized. In most developed countries, CCTV surveillance systems are installed in almost every public location to take periodic images of a specific area. Notwithstanding, cameras are used under different types of ambient light, and they experience occlusions, distortions of view, and changes in the resulting images from different camera angles and the different seasons of the year, all of which affect the accuracy of currently established models. To address these problems, we developed an approach based on an attention feature map used in a capsule network designed to classify fire and smoke locations at different distances outdoors, given only an image of a single fire and smoke as input. The proposed model was designed to solve two main limitations of the base capsule network input and the analysis of large-sized images, as well as to compensate the absence of a deep network using an attention-based approach to improve the classification of the fire and smoke results. In term of practicality, our method is comparable with prior strategies based on machine learning and deep learning methods. We trained and tested the proposed model using our datasets collected from different sources. As the results indicate, a high classification accuracy in comparison with other modern architectures was achieved. Further, the results indicate that the proposed approach is robust and stable for the classification of images from outdoor CCTV cameras with different viewpoints given the presence of smoke and fire.

Criteria for comparing artificial intelligence systems

Модели нейронных и нейро-нечетких сетевых критериев сравнения в задачах диагностики и классификации образов. Предложен комплекс критериев для оценки свойств искусственных нейронных и нейро-нечетких сетей. Он включает в себя критерии разнообразия, подгонки, эластичности, равнозначности, устойчивости к шуму, аварийной ситуации, а также заданную монотонность для построения нейронной модели. Применение предложенных критериев на практике позволяет автоматизировать процесс построения, анализа и сравнения нейронных моделей для решения задач диагностики и классификации паттернов. Предложено решение задачи повышения эффективности параметрического синтеза нейросетевых моделей сложных систем для обоснованного принятия решений о классификации подводных целей. Научная новизна работы заключается в том, что впервые предложен комплекс моделей критериев, характеризующих такие свойства нейронных и нейро-нечетких сетей как разнообразие, переобученность, эластичность, эквифинальность, устойчивость к шуму, эмерджентность, что позволяет автоматизировать решение задачи анализа свойств и сравнения нейросетевых и нейро-нечетких моделей при решении задач диагностики и классификации образов. В работе решена актуальная задача автоматизации анализа свойств и сравнения нейросетевых моделей. Models of neural and neuro-fuzzy network comparison criterions in the tasks of diagnostics and pattern classification. The complex of criterions for an estimation of properties artificial neural and neuro-fuzzy networks is proposed. It includes criterions of variety, overfitting, elasticity, equifinality, stability to a noise, emergency, and also set monotonicity for a neural model construction. The application of offered criterions in practice allows to automatize the process of a construction, analysis and comparison of neural models for problem solving of diagnostics and patternt classification. The solution of the problem of increasing the efficiency of parametric synthesis of neural network models of complex systems for informed decision-making on the classification of underwater targets is proposed. The scientific novelty of the work lies in the fact that for the first time a set of models of criteria characterizing such properties of neural and neuro-fuzzy networks as diversity, retraining, elasticity, equifinality, noise resistance, emergence is proposed, which allows automating the solution of the problem of analyzing the properties and comparing neural network and neuro-fuzzy models when solving problems of diagnostics and classification of images. The paper solves the actual problem of automating the analysis of properties and comparison of neural network models.

A Review of Breast Cancer Histopathological Image Classification

Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed.

Intelligent system for assessing the harmfulness of food products based on the processing of textual and graphic information

The paper substantiates the need to assess the harm of food for consumers with chronic diseases or allergies, which is important to prevent possible deterioration of the disease or eliminate acute allergic reactions of the human body to harmful ingredients present in the product. It is proved that currently there is no convenient intelligent system that could recognize the composition of products on the Ukrainian market, provide product characteristics and assess the harmfulness of the product. It is proposed to use food labels and packaging as primary sources of food information that is available to the consumer. It is shown that the printed information on the packages is presented in text-graphic form. The development of a mobile system as a software solution for the detection and analysis of textual and graphical information on the composition of products based on the use of artificial intelligence methods is proposed and substantiated. The block diagram of the intelligent mobile system for detection and analysis of food composition has been developed. The MSER algorithm is used to select text regions on the input image matrix in the presented algorithmic software. The solution to the problem of character recognition was based on the use of convolutional neural network MobileNet-V2, which is currently the best option in the classification of images by mobile applications that do not have a server part, and therefore large computing resources. Alignment of text on the image was carried out using the method of finding a rectangle with the smallest area Developed algorithms for grouping words. A decision support algorithm has been proposed to assess the harmfulness of products. The developed system allows personalized selection of food for each individual user, ie, the assessment of the composition of products is calculated taking into account the state of health of use, existing threats, diseases, restrictions or allergies

DETECTION OF LANE LINES FOR SELFDRIVING CARS BY USING EFFECTIVE COMPUTER VISION TECHNIQUES WITH OPENCV IN PYTHON

Presently computer vision is amongst the hottest topics in Artificial Intelligence and is being extensively used in Robotics, Detecting Objects, Classification of Images, Autonomous Vehicles & tracking, Semantic Segmentation along with photo correction in various apps. In Self driven cars/ vehicles, vision remains the main source of information for detecting lanes, traffic lights, pedestrian crossing and other visual features. [2]

Galaxy Morphological Image Classification using ResNet

Machine learning-based techniques are used widely for the classification of images into various categories. The advancement of Convolutional Neural Network (CNN) affects the field of computer vision on a large scale. It has been applied to classify and localize objects in images. Among the fields of applications of CNN, it has been applied to understand huge unstructured astronomical data being collected every second. Galaxies have diverse and complex shapes and their morphology carries fundamental information about the whole universe. Studying these galaxies has been a tremendous task for the researchers around the world. Researchers have already applied some basic CNN models to predict the morphological classes of the galaxies. In this paper, a residual network (ResNet) model is applied for this purpose. The proposed methodology classified the galaxies depending on their shape into 37 different classes. The performance of the methodology was evaluated using the data set provided by Kaggle. In this data set, 61,578 galaxy images are given, which are classified by human eye. The model achieved nearly 98% accuracy.

A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks

Measuring the predictability and complexity of time series using entropy is essential tool designing and controlling a nonlinear system. However, the existing methods have some drawbacks related to the strong dependence of entropy on the parameters of the methods. To overcome these difficulties, this study proposes a new method for estimating the entropy of a time series using the LogNNet neural network model. The LogNNet reservoir matrix is filled with time series elements according to our algorithm. The accuracy of the classification of images from the MNIST-10 database is considered as the entropy measure and denoted by NNetEn. The novelty of entropy calculation is that the time series is involved in mixing the input information in the reservoir. Greater complexity in the time series leads to a higher classification accuracy and higher NNetEn values. We introduce a new time series characteristic called time series learning inertia that determines the learning rate of the neural network. The robustness and efficiency of the method is verified on chaotic, periodic, random, binary, and constant time series. The comparison of NNetEn with other methods of entropy estimation demonstrates that our method is more robust and accurate and can be widely used in practice.