Detection of COVID-19, pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation.

Chest radiography is an essential diagnostic tool for respiratory diseases such as COVID-19, pneumonia, and tuberculosis because it accurately depicts the structures of the chest. However, accurate detection of these diseases from radiographs is a complex task that requires the availability of medical imaging equipment and trained personnel. Conventional deep learning models offer a viable automated solution for this task. However, the high complexity of these models often poses a significant obstacle to their practical deployment within automated medical applications, including mobile apps, web apps, and cloud-based platforms. This study addresses and resolves this dilemma by reducing the complexity of neural networks using knowledge distillation techniques (KDT). The proposed technique trains a neural network on an extensive collection of chest X-ray images and propagates the knowledge to a smaller network capable of real-time detection. To create a comprehensive dataset, we have integrated three popular chest radiograph datasets with chest radiographs for COVID-19, pneumonia, and tuberculosis. Our experiments show that this knowledge distillation approach outperforms conventional deep learning methods in terms of computational complexity and performance for real-time respiratory disease detection. Specifically, our system achieves an impressive average accuracy of 0.97, precision of 0.94, and recall of 0.97.

Improved Electrochemical Performance in an Exfoliated Tetracyanonickelate-Based Metal-Organic Framework.

Tetracyanonickelate (TCN)-based metal-organic frameworks (MOFs) show great potential in electrochemical applications such as supercapacitors due to their layered morphology and tunable structure. This study reports on improved electrochemical performance of exfoliated manganese tetracyanonickelate (Mn-TCN) nanosheets produced by the heat-assisted liquid-phase exfoliation (LPE) technique. The structural change was confirmed by the Raman frequency shift of the C≡N band from 2177 to 2182 cm-1 and increased band gap from 3.15 to 4.33 eV in the exfoliated phase. Statistical distribution obtained from atomic force microscopy (AFM) shows that 50% of the nanosheets are single-to-four-layered and have an average lateral size of ∼240 nm2 and thickness of ∼1.2-4.8 nm. High-resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED) patterns suggest that the material maintains its crystallinity after exfoliation. It exhibits an almost 6-fold improvement in specific capacitance (from 13.0 to 72.5 F g-1) measured at a scan rate of 5 mV s-1 in 1 M KOH solution. Galvanostatic charge-discharge (GCD) measurement shows a capacity enhancement from ∼18 F g-1 in the bulk phase to ∼45 F g-1 in the exfoliated phase at a current density of 1 A g-1. Bulk crystals exhibit an increasing trend of capacitance retention by ∼125% over 1000 charge-discharge cycles attributed to electrochemical exfoliation. Electrochemical impedance spectroscopy (EIS) demonstrates a 5-fold reduction in the total equivalent series resistance (ESR) from 4864 Ω (bulk) to 1089 Ω (exfoliated). The enhanced storage capacity in the exfoliated phase results from the combined effect of the electrochemical double-layer charge storage mechanism at the nanosheet-electrolyte interface and the Faradic process characteristic of the pseudocapacitive charge storage behavior.

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.

AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval.

Image retrieval techniques are becoming famous due to the vast availability of multimedia data. The present image retrieval system performs excellently on labeled data. However, often, data labeling becomes costly and sometimes impossible. Therefore, self-supervised and unsupervised learning strategies are currently becoming illustrious. Most of the self/unsupervised strategies are sensitive to the number of classes and can not mix labeled data on availability. In this paper, we introduce AutoRet, a deep convolutional neural network (DCNN) based self-supervised image retrieval system. The system is trained on pairwise constraints. Therefore, it can work in self-supervision and can also be trained on a partially labeled dataset. The overall strategy includes a DCNN that extracts embeddings from multiple patches of images. Further, the embeddings are fused for quality information used for the image retrieval process. The method is benchmarked with three different datasets. From the overall benchmark, it is evident that the proposed method works better in a self-supervised manner. In addition, the evaluation exhibits the proposed method's performance to be highly convincing while a small portion of labeled data are mixed on availability.

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis.

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

Exploring optimal control of epidemic spread using reinforcement learning.

Pandemic defines the global outbreak of a disease having a high transmission rate. The impact of a pandemic situation can be lessened by restricting the movement of the mass. However, one of its concomitant circumstances is an economic crisis. In this article, we demonstrate what actions an agent (trained using reinforcement learning) may take in different possible scenarios of a pandemic depending on the spread of disease and economic factors. To train the agent, we design a virtual pandemic scenario closely related to the present COVID-19 crisis. Then, we apply reinforcement learning, a branch of artificial intelligence, that deals with how an individual (human/machine) should interact on an environment (real/virtual) to achieve the cherished goal. Finally, we demonstrate what optimal actions the agent perform to reduce the spread of disease while considering the economic factors. In our experiment, we let the agent find an optimal solution without providing any prior knowledge. After training, we observed that the agent places a long length lockdown to reduce the first surge of a disease. Furthermore, the agent places a combination of cyclic lockdowns and short length lockdowns to halt the resurgence of the disease. Analyzing the agent's performed actions, we discover that the agent decides movement restrictions not only based on the number of the infectious population but also considering the reproduction rate of the disease. The estimation and policy of the agent may improve the human-strategy of placing lockdown so that an economic crisis may be avoided while mitigating an infectious disease.

Combining ability of pod yield and related traits of groundnut (Arachis hypogaea L.) under salinity stress.

A study was performed using 6 × 6 F1 diallel population without reciprocals to assess the mode of inheritance of pod yield and related traits in groundnut with imposed salinity stress. Heterosis was found for pod number and yield. Data on general and specific combining ability (gca and sca) indicated additive and nonadditive gene actions. The gca: sca ratios were much less than unity suggesting predominant role of nonadditive gene effects. Cultivars "Binachinabadam-2" and "Dacca-1" and mutant M6/25/64-82 had the highest, second highest, and third highest pod number, as well as gca values, respectively. These two cultivars and another mutant M6/15/70-19 also had the highest, second highest, and third highest pod yield, as well as gca values, respectively. Therefore, "Dacca-1", "Binachinabadam-2", M6/25/64-82, and M6/15/70-19 could be used as source of salinity tolerance. Cross combinations showing high sca effects arising from parents with high and low gca values for any trait indicate the influence of nonadditive genes on their expression. Parents of these crosses can be used for biparental mating or reciprocal recurrent selection for developing high yielding varieties. Crosses with high sca effects having both parents with good gca effects could be exploited by pedigree breeding to get transgressive segregants.

A schedule-based multi-channel MAC protocol for wireless sensor networks.

Due to the half-duplex property of the sensor radio and the broadcast nature of wireless medium, limited bandwidth remains a pressing issue for wireless sensor networks (WSNs). The design of multi-channel MAC protocols has attracted the interest of many researchers as a cost effective solution to meet the higher bandwidth demand for the limited bandwidth in WSN. In this paper, we present a scheduled-based multi-channel MAC protocol to improve network performance. In our protocol, each receiving node selects (schedules) some timeslot(s), in which it may receive data from the intending sender(s). The timeslot selection is done in a conflict free manner, where a node avoids the slots that are already selected by others in its interference range. To minimize the conflicts during timeslot selection, we propose a unique solution by splitting the neighboring nodes into different groups, where nodes of a group may select the slots allocated to that group only. We demonstrate the effectiveness of our approach thorough simulations in terms of performance parameters such as aggregate throughput, packet delivery ratio, end-to-end delay, and energy consumption.