# MiNet: A Convolutional Neural Network for Identifying and Categorising Minerals Source: [MiNet: A Convolutional Neural Network for Identifying and Categorising Minerals]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:50.747Z Authors: Emmanuel Asiedu Brempong, Millicent Agangiba, Daniel Aikins Published: 2021-11-22T15:00:28.000Z ## Abstract Identification of minerals in the field is a task that is wrought with many challenges. Traditional approaches are prone to errors where there is no enough experience and expertise. Several existing techniques mainly make use of features of the minerals under a microscope and tend to favour a manual feature extraction pipeline. Deep learning methods can help overcome some of these hurdles and provide simple and effective ways to identify minerals. In this paper, we present an algorithm for identifying minerals from hand specimen images. Using a Convolutional Neural Network (CNN), we develop a single-label image classification model to identify and categorise seven classes of minerals. Experiments conducted using real-world datasets show that the model achieves an accuracy of 90.75%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()