An optimized convolutional neural network architecture for lung cancer detection.

Lung cancer, the treacherous malignancy affecting the respiratory system of a human body, has a devastating impact on the health and well-being of an individual. Due to the lack of automated and noninvasive diagnostic tools, healthcare professionals look forward toward biopsy as a gold standard for diagnosis. However, biopsy could be traumatizing and expensive process. Additionally, the limited availability of dataset and inaccuracy in diagnosis is a major drawback experienced by researchers. The objective of the proposed research is to develop an automated diagnostic tool for screening of lung cancer using optimized hyperparameters such that convolutional neural network (CNN) model generalizes well for universally obtained computerized tomography (CT) slices of lung pathologies. The aforementioned objective is achieved in the following ways: (i) Initially, a preprocessing methodology specific to lung CT scans is formulated to avoid the loss of information due to random image smoothing, and (ii) a sine cosine algorithm optimization algorithm (SCA) is integrated in the CNN model, to optimally select the tuning parameters of CNN. The error rate is used as an objective function, and the SCA algorithm tries to minimize. The proposed method successfully achieved an average classification accuracy of 99% in classification of lung scans in normal, benign, and malignant classes. Further, the generalization ability of the proposed model is tested on unseen dataset, thereby achieving promising results. The quantitative results prove the efficacy of the system to be used by radiologists in a clinical scenario.

A Compact MIMO Antenna Based on Modal Analysis for 5G Wireless Applications.

This article presents a planar, non-angular, series-fed, dual-element dipole array MIMO antenna operating at 28 GHz with the metasurface-based isolation improvement technique. The initial design is a single-element antenna with a dual dipole array which is series-fed. These dipole elements are non-uniform in shape and distance. This dipole antenna results in end-fire radiation. The dipole antenna excites the J1 mode for its operation. Further, with the view to improve channel capacity, the dipole array expands the antenna to a three-element MIMO antenna. In the MIMO antenna structure, the sum of the J1, J2, and J3 modes is excited, causing resonance at 28 GHz. This article also proposes a metasurface structure with wide stopband characteristics at 28 GHz for isolation improvement. The metasurface is composed of rectangle-shaped structures. The defected ground and metasurface structure combination suppresses the surface wave coupling among the MIMO elements. The proposed antenna results in a bandwidth ranging from 26.7 to 29.6 GHz with isolation improvement greater than 21 dB and a gain of 6.3 dBi. The antenna is validated with the diversity parameters of envelope correlation coefficient, diversity gain, and channel capacity loss.

A Low-Profile Circularly Polarized Millimeter-Wave Broadband Antenna Analyzed with a Link Budget for IoT Applications in an Indoor Scenario.

Broadband antennas with a low-profile generating circular polarization are always in demand for handheld/ portable devices as CP antennas counter multipath and misalignment issues. Therefore, a compact millimeter-wave antenna is proposed in this article. The proposed antenna structure comprises two circular rings and a circular patch at the center. This structure is further embedded with four equilateral triangles at a 90° orientation. The current entering the radiator is divided into left and right circular directions. The equilateral triangles provide the return path for current at the differential phase of ±90°, generating circular polarization. Structural development and analysis were initially performed through the characteristic mode theory. It showed that Modes 1 to 4 generated good impedance matching from 20 to 30 GHz and Modes 1 to 5, from 30 to 40 GHz. It also demonstrated the summation of orthogonal modes leading to circular polarization. The antenna-measured reflection coefficient |S11| > 10 dB was 19 GHz (23-42 GHz), and the axial ratio at -3 dB was 4.2 GHz (36-40.2 GHz). The antenna gain ranged from 4 to 6.2 dBi. The proposed antenna was tested for link margin estimation for IoT indoor conditions with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The communication reliability with co- and cross-polarization was also studied under these conditions, and the results proved to be satisfactory.

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids.

Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.

A Compact Dual-Band Millimeter Wave Antenna for Smartwatch and IoT Applications with Link Budget Estimation.

Advancement in smartwatch sensors and connectivity features demands low latency communication with a wide bandwidth. ISM bands below 6 GHz are reaching a threshold. The millimeter-wave (mmWave) spectrum is the solution for future smartwatch applications. Therefore, a compact dual-band antenna operating at 25.5 and 38 GHz is presented here. The characteristics mode theory (CMT) aids the antenna design process by exciting Mode 1 and 2 as well as Mode 1-3 at their respective bands. In addition, the antenna structure generates two traverse modes, TM10 and TM02, at the lower and higher frequency bands. The antenna measured a bandwidth (BW) of 1.5 (25-26.5 GHz) and 2.5 GHz (37-39.5 GHz) with a maximum gain of 7.4 and 7.3 dBi, respectively. The antenna performance within the watch case (stainless steel) showed a stable |S11| with a gain improvement of 9.9 and 10.9 dBi and a specific absorption rate (SAR) of 0.063 and 0.0206 W/kg, respectively, at the lower and higher bands. The link budget analysis for various rotation angles of the watch indicated that, for a link margin of 20 dB, the antenna can transmit/receive 1 Gbps of data. However, significant fading was noticed at certain angles due to the shadowing effect caused by the watch case itself. Nonetheless, the antenna has a workable bandwidth, a high gain, and a low SAR, making it suitable for smartwatch and IoT applications.

Flexible Antennas for a Sub-6 GHz 5G Band: A Comprehensive Review.

The ever-increasing demand and need for high-speed communication have generated intensive research in the field of fifth-generation (5G) technology. Sub-6 GHz 5G mid-band spectrum is the focus of the researchers due to its meritorious ease of deployment in the current scenario with the already existing infrastructure of the 4G-LTE system. The 5G technology finds applications in enormous fields that require high data rates, low latency, and stable radiation patterns. One of the major sectors that benefit from the outbreak of 5G is the field of flexible electronics. Devices that are compact need an antenna to be flexible, lightweight, conformal, and still have excellent performance characteristics. Flexible antennas used in wireless body area networks (WBANs) need to be highly conformal to be bent according to the different curvatures of the human body at different body parts. The specific absorption rate (SAR) must be at a permissible level for such an antenna to be suited for WBAN applications. This paper gives a comprehensive review of the current state of the art flexible antennas in a sub-6 GHz 5G band. Furthermore, this paper gives a key insight into the materials for a flexible antenna, the parameters considered for the design of a flexible antenna for 5G, the challenges for the design, and the implementation of a flexible antenna for 5G.

Efficacy of 810 nm diode laser in hair reduction in Indian subpopulation using a novel Gabor filter-based detection software and hair algorithm.

BACKGROUND: Diode laser (810 nm) is frequently employed for hair reduction. There are few studies determining the efficacy in Indian population. OBJECTIVES: Assessment of efficacy and safety of 810 nm diode laser in facial/axillary hair reduction and objective assessment of the improvement with dermoscopy, photographs, and novel Gabor filter-based hair detection algorithm. METHODOLOGY: This hospital-based study included 40 adult women with 108 treatment sites over 5.4 sessions (range 4-8). Evaluation of treatment areas (hair texture, density) was done using modified Ferriman-Galwey scoring. Photography and dermoscopic images were taken before each session and 6 weeks after the last. Immediate and delayed adverse reactions were noted. Assessment of efficacy was done by patient, principal, and blinded investigator using Global Aesthetic Improvement scale (GAIS) and hair detection algorithm (evaluating characteristics of dermoscopic hair). RESULTS: The fluences ranged from 16 to 29 Joules/cm2 with pulse width of 30 ms. Upper lip (n = 29, 26.9%) and chin (n = 25, 22.1%) were commonly treated areas. Improvement in hair texture and density (reduction in uniformly distributed, terminal hair from 37.1% to 13.9%) was statistically significant (p < 0.0001). Excellent improvement of 75-100% (GAIS) was noted by principal and blinded investigator in 24.1% and 33.3% total sites, respectively. The median improvement, calculated by the algorithm, was 60% for side locks, 53.9% for axilla, 24.1% for upper lip, and 14.9% for chin. Axilla and upper lip were sites associated with maximum discomfort. Epidermolysis and paradoxical hypertrichosis were seen in five patients each. CONCLUSION: The 810 nm diode laser is safe and effective in the reduction of dark, coarse terminal hairs in Fitzpatrick skin types III-V. Inter-observer variation and investigator bias in the assessment of efficacy can be successfully overcome by using the algorithm.

An Ultra-Compact 28 GHz Arc-Shaped Millimeter-Wave Antenna for 5G Application.

The 5th generation (5G) network was planned to provide a fast, stable, and future-proof mobile communication network to existing society. This research presents a highly compact arc shape structure antenna resonating at 28 GHz for prospective millimeter-wave purposes in the 5G frequency spectrum. The circular monopole antenna is designed with a radius of 1.3 mm. An elliptical slot on the radiating plane aids in achieving an enhanced bandwidth resonating at the frequency of 28 GHz. Including an elliptical slot creates new resonance and helps improve the bandwidth. The antenna has an ultra-compact dimension of 5 × 3 × 1.6 mm3, which corresponds to an electrical length of 0.46λ × 0.28λ × 0.14λ, where λ is free space wavelength at the resonant frequency. The projected antenna has an impedance bandwidth of 15.73 % (25.83-30.24 GHz). The antenna has a good radiation efficiency of 89%, and the average gain is almost 4 dB over the entire impedance bandwidth. The simulated and experimental S11 findings are in good agreement.

A 20-44 GHz Wideband LNA Design Using the SiGe Technology for 5G Millimeter-Wave Applications.

This paper presents the design and implementation of a low-noise amplifier (LNA) for millimeter-wave (mm-Wave) 5G wireless applications. The LNA was based on a common-emitter configuration with cascode amplifier topology using an IHP's 0.13 µm Silicon Germanium (SiGe) heterojunction bipolar transistor (HBT) whose f_T/f_MAX/gate-delay is 360/450 GHz/2.0 ps, utilizing transmission lines for simultaneous noise and input matching. A noise figure of 3.02-3.4 dB was obtained for the entire wide bandwidth from 20 to 44 GHz. The designed LNA exhibited a gain (S_21) greater than 20 dB across the 20-44 GHz frequency range and dissipated 9.6 mW power from a 1.2 V supply. The input reflection coefficient (S_11) and output reflection coefficient (S_22) were below -10 dB, and reverse isolation (S_12) was below -55 dB for the 20-44 GHz frequency band. The input 1 dB (P1dB) compression point of -18 dBm at 34.5 GHz was obtained. The proposed LNA occupies only a 0.715 mm2 area, with input and output RF (Radio Frequency) bond pads. To the authors' knowledge, this work evidences the lowest noise figure, lowest power consumption with reasonable highest gain, and highest bandwidth attained so far at this frequency band in any silicon-based technology.

Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images.

The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application.

Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture.

The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of lesser sensitivity testing kits, and limited availability of healthcare professionals, the pandemic has spread like wildfire across the world. Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. This is where artificial intelligence meets the diagnostic capabilities of experienced clinicians. The objective of the proposed research is to contribute towards fighting the global pandemic by developing an automated image analysis module for identifying covid-19 affected chest X-ray scans by employing an optimized Convolution Neural Network (CNN) model. The aforementioned objective is achieved in the following manner by developing three classification models, (i) ensemble of ResNet 50-Error Correcting Output Code (ECOC) model, (ii) CNN optimized using Grey Wolf Optimizer (GWO) and, (iii) CNN optimized using Whale Optimization + BAT algorithm. The novelty of the proposed method lies in the automatic tuning of hyper parameters considering a hierarchy of MultiLayer Perceptron (MLP), feature extraction, and optimization ensemble. A 100% classification accuracy was obtained in classifying covid-19 images. Classification accuracy of 98.8% and 96% were obtained for dataset 1 and dataset 2 respectively for classification into covid-19, normal, and viral pneumonia cases. The proposed method can be adopted in a clinical setting for assisting radiologists and it can also be employed in remote areas to facilitate the faster screening of affected patients.

Hair detection and lesion segmentation in dermoscopic images using domain knowledge.

Automated segmentation and dermoscopic hair detection are one of the significant challenges in computer-aided diagnosis (CAD) of melanocytic lesions. Additionally, due to the presence of artifacts and variation in skin texture and smooth lesion boundaries, the accuracy of such methods gets hampered. The objective of this research is to develop an automated hair detection and lesion segmentation algorithm using lesion-specific properties to improve the accuracy. The aforementioned objective is achieved in two ways. Firstly, a novel hair detection algorithm is designed by considering the properties of dermoscopic hair. Second, a novel chroma-based geometric deformable model is used to effectively differentiate the lesion from the surrounding skin. The speed function incorporates the chrominance properties of the lesion to stop evolution at the lesion boundary. Automatic initialization of the initial contour and chrominance-based speed function aids in providing robust and flexible segmentation. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. Average accuracy, sensitivity, specificity, and overlap scores of 93.4, 87.6, 95.3, and 11.52% respectively are obtained for the PH2 dataset. Similarly, the proposed method resulted in average accuracy, sensitivity, specificity, and overlap scores of 94.6, 82.4, 97.2, and 7.20% respectively for the ISBI 2016 dataset. Statistical and quantitative analyses prove the reliability of the algorithm for incorporation in CAD systems. Graphical Abstract Overview of proposed system.