The goal of this study is to classify microalgae of different species such as Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis, using machine learning (ML) and deep learning (DL) methods

Identification of plants by analyzing their leaves using digital image processing techniques which involves examining and extracting various morphological features such as shape, color, and texture. This approach influences technology to automate and streamline the identification process.

A hybrid-feature melanoma detection pipeline combining advanced image preprocessing, handcrafted features, and multiple machine learning classifiers. Designed to deliver accurate and efficient skin cancer recognition.

🩺 Automate melanoma detection using hybrid features and machine learning for accurate skin cancer identification with an efficient coding setup.