Precise Pollen Grain Detection in Bright Field Microscopy Using Deep Learning Techniques.

The determination of daily concentrations of atmospheric pollen is important in the medical and biological fields. Obtaining pollen concentrations is a complex and time-consuming task for specialized personnel. The automatic location of pollen grains is a handicap due to the high complexity of the images to be processed, with polymorphic and clumped pollen grains, dust, or debris.

Low-Power and Low-Cost Environmental IoT Electronic Nose Using Initial Action Period Measurements.

In this work, we present a complete hardware development and current consumption study of a portable electronic nose designed for the Internet-of-Things (IoT). Thanks to the technique of measuring in the initial action period, it can be reliably powered with a moderate-sized battery. The system is built around the well-known SoC (System on Chip) ESP8266EX, using low-cost electronics and standard sensors from Figaro's TGS26xx series.

A compact and low cost electronic nose for aroma detection.

This article explains the development of a prototype of a portable and a very low-cost electronic nose based on an mbed microcontroller. Mbeds are a series of ARM microcontroller development boards designed for fast, flexible and rapid prototyping. The electronic nose is comprised of an mbed, an LCD display, two small pumps, two electro-valves and a sensor chamber with four TGS Figaro gas sensors.

Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction.

Background: Breast cancer continues to be a leading cause of cancer deaths among women, especially in Western countries. In the last two decades, many methods have been proposed to achieve a robust mammography-based computer aided detection (CAD) system. A CAD system should provide high performance over time and in different clinical situations.

Study of a mammographic CAD performance dependence on the considered mammogram set.

This work analyzes the influence of the set of mammograms used in the training processes of a computer aided diagnosis system on the overall performance. We used the mammograms provided by the Digital Database for Screening Mammography, one of the most extended research database. The obtained results seem to suggest an effect on the performance values obtained in a CAD system with different database subsets.

SVM and neural networks comparison in mammographic CAD.

The purpose of this work is to compare the performance of Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) in the task of detection and diagnosis of microcalcification clusters in mammograms (MCCs). As data source, the "Digital Database for Screening Mammography" (DDSM) was used. The results show a similar performance for SVM and MLP, in both tasks, detection and diagnosis (slightly better for MLP in detection).

Microcalcifications detection in digital mammograms.

A method is presented for detecting minimally sized microcalcifications on ma mammograms to add extra security to the radiologist's classification. The method imitates the normal procedure followed by the specialist, and is easily implemented on low-cost PCs. As input, it accepts the usual digital mammograms.