Abstract: The medical diagnostic system comprises a deep neural network model trained with catheter hardness data, lesion hardness data, and operation time to complete catheter treatment; an irradiation energy emitting device to calculate irradiation energy of a patient; a control unit to identify a plurality of lesions from irradiation energy data; and a catheter to insert into an artery in the patient's arm, wherein the catheter tip is positioned to at least the patient's aortailiac bifurcation, wherein a therapeutic catheter is introduced into a catheter lumen and the therapeutic catheter tip is projected from the catheter tip thereby the harder lesion is initially treated, and the therapeutic catheter tip of the therapeutic catheter is projected from the catheter tip to treat the softer lesion.

Abstract: The present invention generally relates to a plant disease detection system comprises an acquisition unit for collecting pictures of sick and sound plant leaves gathered under controlled conditions from a public dataset; a training unit for training a convolutional neural network technique-based model to distinguish harvest species and sicknesses; a camera for capturing real time image of a plant; a central processing unit for estimating weight file (BMI) and muscle versus fat ratio status (BF %) utilizing bio impedance in order to detect disease of the plant, wherein the weight file (BMI) and muscle versus fat ratio status (BF %) is estimated upon comparing real time image of the plant with pictures of sick and sound plant leaves gathered under controlled conditions; and an alert unit for transferring an alert signal of the detected disease and its stage on a user computing device.

Abstract: The present disclosure generally relates to a method for COVID-19 detection for spread prevention and medical assistance using machine learning comprises pre-processing a CT image set for removing noise using HU values; performing a region-based segmentation using a three-dimensional convolutional neural network model; classifying each segmented region by employing a classification model; and comparing classified segmented region with a dataset for detecting COVID-19 using a machine learning approach thereby generating an alert about the COVID-19 positive person.