Neural Architecture Search for biomedical image classification: A comparative study across data modalities.

Deep neural networks have significantly advanced medical image classification across various modalities and tasks. However, manually designing these networks is often time-consuming and suboptimal. Neural Architecture Search (NAS) automates this process, potentially finding more efficient and effective models.

Evolutionary Architecture Optimization for Retinal Vessel Segmentation.

Retinal vessel segmentation (RVS) is crucial in medical image analysis as it helps identify and monitor retinal diseases. Deep learning approaches have shown promising results for RVS, but designing optimal neural network architecture is challenging and time-consuming. Neural architecture search (NAS) is a recent technique that automates the design of neural network architectures within a predefined search space.

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis.

Precise and quick monitoring of key cytometric features such as cell count, size, morphology, and DNA content is crucial in life science applications. Traditionally, image cytometry relies on visual inspection of hemocytometers. This approach is error-prone due to operator subjectivity.

Recommending healthy meal plans by optimising nature-inspired many-objective diet problem.

Healthy eating is an important issue affecting a large part of the world population, so human diets are becoming increasingly popular, especially with the devastating consequences of Coronavirus Disease (Covid-19). A realistic and sustainable diet plan can help us to have a healthy eating habit since it considers most of the expectations from a diet without any restriction. In this study, the classical diet problem has been extended in terms of modelling, data sets and solution approach.