Block-based compressive sensing in deep learning using AlexNet for vegetable classification.

Vegetables can be distinguished according to differences in color, shape, and texture. The deep learning convolutional neural network (CNN) method is a technique that can be used to classify types of vegetables for various applications in agriculture. This study proposes a vegetable classification technique that uses the CNN AlexNet model and applies compressive sensing (CS) to reduce computing time and save storage space.

A Novel Texture Extraction-Based Compressive Sensing for Lung Cancer Classification.

Background: Lung cancer images require large memory storage and transmission bandwidth for sending the data. Compressive sensing (CS), as a method with a statistical approach in signal sampling, provides different output patterns based on information sources. Thus, it can be considered that CS can be used for feature extraction of compressed information.

Feasibility study of strengthening the public-private partnership for tuberculosis case detection in Bandung City, Indonesia.

Objective: Private practitioner's (PPs) collaboration for detection, diagnosis and treatment of tuberculosis (TB) is recommended by the World Health Organization and encouraged by the Indonesian National TB control programme. TB case management by PPs, however, are mostly not in line with current guidelines. Therefore, we developed an intervention package for PPs comprising of TB training, implementation of a mobile phone application for notification of TB cases and a 6-month regular follow-up with PPs.