Performance evaluation of Neural Network models applied to the classification of mineral imagery from the Katanga mining region

Blaise FYAMA; Ruphin NYAMI NYATE

doi:10.36948/ijfmr.2025.v07i01.35127

Performance evaluation of Neural Network models applied to the classification of mineral imagery from the Katanga mining region

Author(s)	Blaise FYAMA, Ruphin NYAMI NYATE
Country	Congo (Democratic)
Abstract	Nowadays, the need for a powerful model in the task of accurate image classification is increasing. This paper focuses on evaluating and comparing the effectiveness of three main neural network models namely the multilayer perceptron model, convolutional neural networks and the transfer learning model for the classification of mineral and rock images in the mining region of Katanga. The main problem addressed is the identification of the most efficient and accurate machine learning techniques in the specific classification of mineral images, with a particular focus on the constitution of the dataset, the analysis of mineral image data and their associated labels. Parameters such as the burial lot, the number of cycles, the size of convolution filters, the precision and the loss have been taken into account. The results show that the transfer learning-based model significantly outperforms the multilayer perceptron models and convolutional neural networks in terms of accuracy and robustness, achieving a classification accuracy of 97.8% compared to 75% for the multilayer perceptron and 96% for the convolutional neural networks designed from scratch. These remarkable results demonstrate the importance of deep learning in processing complex images and open new perspectives for the use of these techniques in the mining sector of the Greater Katanga mining region in the identification of mineral resources. The broader implications of this study include an innovation in mining exploration strategies through faster and more accurate classification of minerals, thus influencing both economic decision-making and environmental policies associated with mining in the region.
Keywords	Performance evaluation, Transfer learning, Deep learning, Convolutional neural networks, Mineral image classification
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-01-26
Cite This	Performance evaluation of Neural Network models applied to the classification of mineral imagery from the Katanga mining region - Blaise FYAMA, Ruphin NYAMI NYATE - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35127
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.35127
Short DOI	https://doi.org/g829sm

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Performance evaluation of Neural Network models applied to the classification of mineral imagery from the Katanga mining region

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