Information Theory and Reliable Communication

Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there are still numerous problems that mask extraction techniques need to solve. Thus, the most advanced methods to deploy artificial intelligence (AI) techniques are necessary. The use of cooperative agents in mask extraction increases the efficiency of automatic image segmentation. Hence, we introduce a new mask extraction method that is based on multi-agent deep reinforcement learning (DRL) to minimize the long-term manual mask extraction and to enhance medical image segmentation frameworks. A DRL-based method is introduced to deal with mask extraction issues. This new method utilizes a modified version of the Deep Q-Network to enable the mask detector to select masks from the image studied. Based on COVID-19 computed tomography (CT) images, we used DRL mask extraction-based techniques to extract visual features of COVID-19 infected areas and provide an accurate clinical diagnosis while optimizing the pathogenic diagnostic test and saving time. We collected CT images of different cases (normal chest CT, pneumonia, typical viral cases, and cases of COVID-19). Experimental validation achieved a precision of 97.12% with a Dice of 80.81%, a sensitivity of 79.97%, a specificity of 99.48%, a precision of 85.21%, an F1 score of 83.01%, a structural metric of 84.38%, and a mean absolute error of 0.86%. Additionally, the results of the visual segmentation clearly reflected the ground truth. The results reveal the proof of principle for using DRL to extract CT masks for an effective diagnosis of COVID-19.

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A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation

Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.

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TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification of BC are very important. Furthermore, manual detection is time-consuming, laborious work, and, possibility of pathologist errors, and incorrect classification. To address the above highlighted issues, this paper presents a hybrid deep learning (CNN-GRU) model for the automatic detection of BC-IDC (+,−) using whole slide images (WSIs) of the well-known PCam Kaggle dataset. In this research, the proposed model used different layers of architectures of CNNs and GRU to detect breast IDC (+,−) cancer. The validation tests for quantitative results were carried out using each performance measure (accuracy (Acc), precision (Prec), sensitivity (Sens), specificity (Spec), AUC and F1-Score. The proposed model shows the best performance measures (accuracy 86.21%, precision 85.50%, sensitivity 85.60%, specificity 84.71%, F1-score 88%, while AUC 0.89 which overcomes the pathologist’s error and miss classification problem. Additionally, the efficiency of the proposed hybrid model was tested and compared with CNN-BiLSTM, CNN-LSTM, and current machine learning and deep learning (ML/DL) models, which indicated that the proposed hybrid model is more robust than recent ML/DL approaches.

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Intelligent Hybrid Deep Learning Model for Breast Cancer Detection

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and Technology

A Framework for Online Resources and E-Learning Implementation (OREI) in Tanzania Secondary Schools

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

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Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Предметом вивчення є процеси забезпечення безпеки інформації в безпроводових каналах управління та зв’язку системи Internet of Things (IoT). Мета – розробка концепції організації широкосмугового доступу до Інтернету та реалізація ключових компетенцій безпеки під час виконання проектів IoT. В основу моделі покладено еталонну модель DigComp 2.0, яку створено в рамках системи цифрової компетентності громадян. Причому, основною сферою компетентності цієї моделі є безпека. Задача – забезпечення усталеної та безпечної роботи системи IoT. Використані методи : методи аналітичного, імітаційного моделювання та часового позиційно-імпульсного кодування. Отримані наступні результати . Розроблена концепція забезпечення безпеки в безпроводових каналах управління та зв’язку системи IoT. Показано, що для отримання високої завадостійкості каналів управління і зв’язку та захисту інформації від перехоплення слід застосовувати технологію безпроводового надширокосмугового зв’язку, яка дозволяє забезпечити великі обсяги та швидкості передачі інформації. Обґрунтовано можливість сумісної беззавадової роботи в одному частотному діапазоні як традиційних вузькосмугових систем зв’язку, так і систем, які використовують надширокосмугові сигнали. Запропоновано технічне рішення щодо конструкції надширокосмугової приймально-передавальної антенної системи. Висновки. Використання каналів із надширокою смугою частот дає можливість практично необмеженого збільшення кількості каналів управління та зв’язку системи IoT. Попередній розподіл між каналами ортогональних кодів реалізує процес управління та зв’язку без перехоплення інформації та взаємних завад. Причому, використання методу часового позиційно-імпульсного кодування запобігає виникненню міжсимвольних спотворень кодуючих надкоротких імпульсів. При цьому також знижується рівень спотворень інформаційних сигналів, які викликані його багатопроменевим розповсюдженням, що гарантує безпеку інформації в системі IoT.

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The concept of information security in the IoT system

Problems of assessing and increasing the efficiency of blended learning environments are discussed. Situation in Lithuanian higher education is presented, its characteristic being in significant efforts to shift from traditional to blended learning, and insufficient efforts to analyze the efficiency of already introduced blended learning environments. Different models of blended learning are discussed and analyzed with respect to the interests of different actors and matched to the Lithuanian situation. Efficiency of the blended learning environments is analyzed using empirical research data. Qualitative diffusion model is used for analyzing the ICT adoption trends in blended learning and suggesting possible solutions.

Efficiency of blended learning environments — Bridging the gap

In the light of technological, economical and social changes, the problems of overall occupation of people and continuous growth of economy become more important and complex. It is universally accepted that continuous development of people's knowledge and skills, investment in education and vocational training are the main factors stimulating the economical and social development of the state. This paper analysis the situation of implementation of distance learning in vocational training system, the organizational and financial problems of distance education and their solving methods.

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Arturas Mickus

Papers

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

The concept of information security in the IoT system

Efficiency of blended learning environments — Bridging the gap

Solving organizational and financial problems of distance education in liedm network