A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease’s influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue’s nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework’s higher accuracy and precision rate

Carotid artery Disease Assessed by Color Doppler Flow Imaging: Comparison Between Diabetic and Non-Diabetic Patients

Background: Carotid artery disease is most often seen in hypertensive patients and in patients with diabetes mellitus. More than 50% stenosis of extra cranial internal carotid arteries is linked with about 8–15% of ischemic strokes. The incidence of carotid artery stenosis (CAS) among diabetic patients is rising as compared to non-diabetic patients.  Methods: A cross-sectional study was performed on 120 patients, out of whom 60 were diabetic and 60 non-diabetics with clinically suspected carotid artery disease.  The study was conducted at the university ultrasound clinic in Green Town by Doppler ultrasonography using the Toshiba XARIO XG, which features a linear probe of 5-7.5 MHz frequency. The data was analyzed with the help of SPSS version 25.0. Variables like age, gender, diabetes, and Intima-media thickness (IMT) were reported and the mean ± standard deviation of Pulsatility Index, Resistive Index, Peak Systolic Velocity, and End Diastolic Velocity were calculated with a significant p-value, which is less than 0.05. An independent t-test was applied to compare Doppler indices in diabetic and non-diabetic subjects.Results: Data was collected from 120 patients. IMT of right and left carotid artery, PI and RI of right carotid were observed to be statistically significant in diabetic and non-diabetic.Conclusions: This study concluded that there is a significant correlation found between carotid artery disease and diabetes. Through ultrasonography, the presence of plaque and stenosis was found in more diabetic patients than in non-diabetic patients.Keywords: Ultrasonography; Carotid artery disease; Carotid artery stenosis; Carotid plaque; Vascular ultrasound; Diabetes   

Crops leaf diseases recognition: a framework of optimum deep learning features

Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time

Two-stream deep learning architecture-based human action recognition

Human action recognition (HAR) based on Artificial intelligence reasoning is the most important research area in computer vision. Big breakthroughs in this field have been observed in the last few years; additionally, the interest in research in this field is evolving, such as understanding of actions and scenes, studying human joints, and human posture recognition. Many HAR techniques are introduced in the literature. Nonetheless, the challenge of redundant and irrelevant features reduces recognition accuracy. They also faced a few other challenges, such as differing perspectives, environmental conditions, and temporal variations, among others. In this work, a deep learning and improved whale optimization algorithm based framework is proposed for HAR. The proposed framework consists of a few core stages i.e., frames initial preprocessing, fine-tuned pre-trained deep learning models through transfer learning (TL), features fusion using modified serial based approach, and improved whale optimization based best features selection for final classification. Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets. The fusion process increases the length of feature vectors; therefore, improved whale optimization algorithm is proposed and selects the best features. The best selected features are finally classified using machine learning (ML) classifiers. Four publicly accessible datasets such as Ut-interaction, Hollywood, Free Viewpoint Action Recognition using Motion History Volumes (IXMAS), and centre of computer vision (UCF) Sports, are employed and achieved the testing accuracy of 100%, 99.9%, 99.1%, and 100% respectively. Comparison with state of the art techniques (SOTA), the proposed method showed the improved accuracy

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM

Signet ring cell detection from histological images using deep learning

Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases. Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task. Automatic detection of SRCs in a patient's body through medical imaging by incorporating computing technologies is a hot topic of research. In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN). In the first step, the input image is fed to Resnet-101 for feature extraction. The extracted feature maps are conveyed to Region Proposal Network (RPN) for the generation of the region of interest (RoI) proposals as well as they are directly conveyed to RoiAlign. Secondly, RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected (FC) network and performs classification along with Bounding Box (bb) generation by using FC layers. The annotations are developed from ground truth (GT) images to perform experimentation on our developed dataset. Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials. We aim to release the employed database soon to assist the improvement in the SRC recognition research area

Medical image colorization for better visualization and segmentation

Medical images contain precious anatomical information for clinical procedures. Improved understanding of medical modality may contribute significantly in arena of medical image analysis. This paper investigates enhancement of monochromatic medical modality into colorized images. Improving the contrast of anatomical structures facilitates precise segmentation. The proposed framework starts with pre-processing to remove noise and improve edge information. Then colour information is embedded to each pixel of a subject image. A resulting image has a potential to portray better anatomical information than a conventional monochromatic image. To evaluate the performance of colorized medical modality, the structural similarity index and the peak signal to noise ratio are computed. Supremacy of proposed colorization is validated by segmentation experiments and compared with greyscale monochromatic images

The importance of alternative host plants as reservoirs of the cotton leaf hopper, Amrasca devastans, and its natural enemies

Many agricultural pests can be harboured by alternative host plants but these can also harbour the pests’ natural enemies. We evaluated the capacity of non-cotton plant species (both naturally growing and cultivated) to function as alternative hosts for the cotton leaf hopper Amrasca devastans (Homoptera: Ciccadellidae) and its natural enemies. Forty-eight species harboured A. devastans. Twenty-four species were true breeding hosts, bearing both nymphal and adult A. devastans, the rest were incidental hosts. The crop Ricinus communis and the vegetables Abelmoschus esculentus and Solanum melongena had the highest potential for harbouring A. devastans and carrying it over into the seedling cotton crop. Natural enemies found on true alternative host plants were spiders, predatory insects (Chrysoperla carnea, Coccinellids, Orius spp. and Geocoris spp.) and two species of egg parasitoids (Arescon enocki and Anagrus sp.). Predators were found on 23 species of alternative host plants, especially R. communis. Parasitoids emerged from one crop species (R. communis) and three vegetable species; with 39 % of A. devastans parasitised. We conclude that the presence of alternative host plants provides both advantages and disadvantages to the cotton agro-ecosystem because they are a source of both natural enemy and pest species. To reduce damage by A. devastans, we recommend that weeds that harbour the pest should be removed, that cotton cultivation with R. communis, A. esculentus, and S. melongena should be avoided, that pesticides should be applied sparingly to cultivate alternative host plants and that cotton crops should be sown earlier

Role of evaporation time on the structural and optical properties of ZnO films deposited by thermal evaporator

Zinc oxide films are deposited on Si substrates by thermal evaporator for different evaporation times (ET). XRD pattern shows the development of different diffraction peaks related to Zn, ZnO and Zn2SiO4 phases which confirms the deposition of composite film. The orientation transformation is observed with increasing ET. The maximum peak intensity of ZnO (1 0 1) plane is observed at 3 h ET. The dislocation density observed in ZnO (1 0 1) plane varies from 1.53 × 10-3 nm-2 to 8.94 × 10-3 nm-2. The lattice parameters of ZnO are found to be a = 3.243 Å and c = 5.197 Å. FTIR analysis confirms the formation of ZnO films. SEM microstructures exhibit the formation nano-wires, nano-bars, nano-strips and nano-needles. The optical energy band gap of ZnO films deposited for various ET varies from 3.98 eV to 4.06 eV. Results show that the peak intensity of ZnO (1 0 1) plane, orientation transformation and the presence of Si content are responsible to increase the energy band gap of ZnO films