The various corn diseases that affect agriculture go unnoticed by farmers. Each day, more crops fail due to diseases as there is no effective treatment or a way to identify the illness. Common rust, blight, and the northern leaf grey spot are the most prevalent corn diseases. The presence of a disease cannot be accurately detected by simply looking at the plant. This will lead to improper pesticide use, which harms people by bringing on chronic diseases. Therefore, maintaining food security depends on accurate and automatic disease detection. It might be possible to save time and stop crop degradation before it takes place by utilising digital technologies. Hence, applying modern digital technologies to identify the disease in the damaged corn fields automatically will be more advantageous to the farmers. Many academics have recently become interested in deep learning, which has aided in creating an exact and autonomous picture classification scheme. The use of deep learning techniques and their adjustments for detecting corn illnesses can greatly assist contemporary agriculture. To find plant leaf diseases, we employ image acquisition, preprocessing, and classification processes. Preprocessing includes procedures such as reading images, resizing images, and data augmentation. The suggested project is based on EfficientNet and improves the precision of the database of corn leaf diseases by tweaking the variables. Tests are run using DenseNet and Resnet on the test dataset to confirm the precision and robustness of this approach. The recognition accuracy of 98.85% that can be achieved using this method, according to experimental results, is significantly higher than those of other cutting-edge techniques.

One of the most common oncologies analyzed among people worldwide is lung malignancy. Early detection of lung malignancy helps find a suitable treatment for saving human lives. Due to its high resolution, greater transparency, and low noise and distortions, Computed Tomography (CT) images are most commonly used for processing. In this context, this research work mainly focused on the multifaceted nature of lung cancer diagnosis, a quintessential, fascinating, and risky subject of oncology. The input used here has been nano-image, enhanced with a Gabor filter and modified color-based histogram equalization. Then, the image of lung cancer was segmented by using the Guaranteed Convergence Particle Swarm Optimization (GCPSO) algorithm. A graphical user interface nano-measuring tool was designed to classify the tumor region. The Bag of Visual Words (BoVW) and a Convolutional Recurrent Neural Network (CRNN) were employed for image classification and feature extraction processes. In terms of findings, we achieved the average precision of 96.5%, accuracy of 99.35%, sensitivity of 97%, specificity of 99% and F1 score of 95.5%. With the proposed solution, the overall time required for the segmentation of images was much smaller than the existing solutions. It is also remarkable that biocompatible-based nanotechnology was developed to distinguish the malignancy region on a nanometer scale and has to be evaluated automatically. That novel method succeeds in producing a proficient, robust, and precise segmentation of lesions in nano-CT images.

Meningiomas are the most prevalent benign intracranial life-threatening brain tumors, with a life expectancy of a few months in the later stages, so this type of tumor in the brain image should be recognized and detected efficiently. The source of meningiomas is unknown. Radiation exposure, particularly during childhood, is the sole recognized environmental risk factor for meningiomas. The imaging technique of magnetic resonance imaging (MRI) is commonly used to detect most tumor forms as it is a non-invasive and painless method. This study introduces a CNN-HHO integrated automated identification model, which makes use of SeaLion optimization methods for improving overall network optimization. In addition to these techniques, various CNN models such as Resnet, VGG, and DenseNet have been utilized to give an overall influence of CNN with SeaLion in each methodology. Each model is tested on our benchmark dataset for accuracy, specificity, dice coefficient, MCC, and sensitivity, with DenseNet outperforming the other models with a precision of 98%. The proposed methods outperform existing alternatives in the detection of brain tumors, according to the existing experimental findings.

Quick Detection of Brain Tumor using a Combination of E-M and Levelset Method

This paper puts forth a framework of a medical image analysis system for brain tumor segmentation. Image segmentation helps to segregate objects right from the background, thus proving to be a powerful tool in medical image processing. This paper presents an improved segmentation algorithm rooted in support vector machine (SVM) and genetic algorithm. SVMs are the basis of a machine-learning technique that has been used for segmentation and classification of medical images. The database consists of two weighted images: T2 and FLAIR. The proposed system mainly consists of two stages. The first stage performs pre-processing the MRI image, followed by block division. The second stage includes feature extraction, feature selection, and finally, the SVM-based training and testing. The feature extraction is done by first-order histogram and co-occurrence matrix; GA using wrapper method is used to select optimum subset features. Experimental outcomes for 10 patients with over 250 MRIs are proof of the proficiency of our work. The performance of the proposed system is evaluated in terms of specificity, sensitivity, accuracy; time elapsed and figure of merit. This approach also permits the segmentation of image volumes based on training sets selected on a single slice.