Identification of major minerals in igneous rock microscopic images from thin sections through deep neural network analysis

Several socio-environmental needs (medicine, industry, engineering, orogenesis, genesis, etc.) require minerals to be more precisly defined and characterised. The identification of minerals plays a crucial role for researchers and is becoming an essential aspect of geological analysis. However, traditional methods relied heavily on expert knowledge and specialised equipment, making them labour-intensive, costly and time-consuming. This dependence is often labour-intensive, not to mention costly and time-consuming. To address this issue, some researchers have opted for machine learning algorithms to quickly identify a single mineral in a microscopic image of rocks. However this approch does not correspond to patterns of mineral distribution, where minerals are typically found in associations. These associations make it difficult to accurately identify minerals using conventional machine learning algorithms. This paper introduces a deep neural learning model based on multi-label classification, utilizing the problem adaptation method to analyse microscopic images of rock thin sections. The model is based on the ResNet50 architecture, which is designed to analyse minerals and generates the probability of a mineral presence in an image. This method provides a solution to the dependence between associated minerals. Experiments on many test images showed a model confidence, achieving average precision, recall and F1_score 97.15 %, 96.25 % and 96.69 %, respectively. Visualisation of the class activation mapping using the Grad-CAM algorithm indicates that our model is likely to locate the identified minerals effectively. In this way, the importance of each pixel with the class of interest can be assessed using heat maps. The recorded results, in terms of both performance and pixel_level evaluation, demonstrate the promising potential of the model used. It can therefore be considered for multi-labels image classification, particulary for images representing rock minerals. This approach serves as a valuable support tool for geological studies.