Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review.

The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.

KUB-UNet: Segmentation of Organs of Urinary System from a KUB X-ray Image.

PURPOSE: The alarming increase in diseases of urinary system is a cause of concern for the populace and health experts. The traditional techniques used for the diagnosis of these diseases are inconvenient for patients, require high cost, and additional waiting time for generating the reports. The objective of this research is to utilize the proven potential of Artificial Intelligence for organ segmentation. Correct identification and segmentation of the region of interest in a medical image are important to enhance the accuracy of disease diagnosis. Also, it improves the reliability of the system by ensuring the extraction of features only from the region of interest. METHOD: A lot of research works are proposed in the literature for the segmentation of organs using MRI, CT scans, and ultrasound images. But, the segmentation of kidneys, ureters, and bladder from KUB X-ray images is found under explored. Also, there is a lack of validated datasets comprising KUB X-ray images. These challenges motivated the authors to tie up with the team of radiologists and gather the anonymous and validated dataset that can be used to automate the diagnosis of diseases of the urinary system. Further, they proposed a KUB-UNet model for semantic segmentation of the urinary system. RESULTS: The proposed KUB-UNet model reported the highest accuracy of 99.18% for segmentation of organs of urinary system. CONCLUSION: The comparative analysis of its performance with state-of-the-art models and validation of results by radiology experts prove its reliability, robustness, and supremacy. This segmentation phase may prove useful in extracting the features only from the region of interest and improve the accuracy diagnosis.

Spatial feature and resolution maximization GAN for bone suppression in chest radiographs.

BACKGROUND AND OBJECTIVE: Chest radiographs (CXR) are in great demand for visualizing the pathology of the lungs. However, the appearance of bones in the lung region hinders the localization of any lesion or nodule present in the CXR. Thus, bone suppression becomes an important task for the effective screening of lung diseases. Simultaneously, it is equally important to preserve spatial information and image quality because they provide crucial insights on the size and area of infection, color accuracy, structural quality, etc. Many researchers considered bone suppression as an image denoising problem and proposed conditional Generative Adversarial Network-based (cGAN) models for generating bone suppressed images from CXRs. These works do not focus on the retention of spatial features and image quality. The authors of this manuscript developed the Spatial Feature and Resolution Maximization (SFRM) GAN to efficiently minimize the visibility of bones in CXRs while ensuring maximum retention of critical information. METHOD: This task is achieved by modifying the architectures of the discriminator and generator of the pix2pix model. The discriminator is combined with the Wasserstein GAN with Gradient Penalty to increase its performance and training stability. For the generator, a combination of different task-specific loss functions, viz., L1, Perceptual, and Sobel loss are employed to capture the intrinsic information in the image. RESULT: The proposed model reported as measures of performance a mean PSNR of 43.588, mean NMSE of 0.00025, mean SSIM of 0.989, and mean Entropy of 0.454 bits/pixel on a test size of 100 images. Further, the combination of δ=104, α=1, ß=10, and γ=10 are the hyperparameters that provided the best trade-off between image denoising and quality retention. CONCLUSION: The degree of bone suppression and spatial information preservation can be improved by adding the Sobel and Perceptual loss respectively. SFRM-GAN not only suppresses bones but also retains the image quality and intrinsic information. Based on the results of student's t-test it is concluded that SFRM-GAN yields statistically significant results at a 0.95 level of confidence and shows its supremacy over the state-of-the-art models. Thus, it may be used for denoising and preprocessing of images.