Bi-layer deep feature fusion based mineral classification using hand-specimen images

被引:0
|
作者
Behera, Santi Kumari [1 ]
Rao, Mannava Srinivasa [2 ]
Amat, Rajat [3 ]
Sethy, Prabira Kumar [4 ,5 ]
机构
[1] VSSUT Burla, Dept Comp Sci & Engn, Sambalpur, India
[2] PVP Siddhartha Inst Technol, Dept Elect & Commun Engn, Vijayawada, Andhra Pradesh, India
[3] Sambalpur Univ, Dept Elect & Commun Engn, SUIIT, Sambalpur, Odisha, India
[4] Sambalpur Univ, Dept Elect, Sambalpur, Odisha, India
[5] Guru Ghasidas Vishwavidyalaya, Dept Elect & Commun Engn, Bilapur, CG, India
关键词
Mineral identification; deep learning; bi-layer feature fusion; deep feature; IDENTIFICATION;
D O I
10.3233/JIFS-221987
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Mineral classification is a crucial task for geologists. Minerals are identified by their characteristics. In the field, geologists can identify minerals by examining lustre, color, streak, hardness, crystal habit, cleavage, fracture, and specific features. Geologists sometimes use a magnifying hand lens to identify minerals in the field. Surface color can assist in identifying minerals. However, it varies widely, even within a single mineral family. Some minerals predominantly show a single color. So, identifying minerals is possible considering surface color and texture. But, again, a limited database of minerals is available with large-scale images. So, the challenges arise to identify the minerals using their images with limited images. With the advancement of machine learning, the deep learning approach with bi-layer feature fusion enhances the dimension of the feature vector with the possibility of high accuracy. Here, an experimental analysis is reported with three possibilities of bi-layer feature fusion of three CNN models like Alexnet, VGG16 & VGG19, and a framework is suggested. Alexnet delivers the highest performance with the bi-layer fusion of fc6 and fc7. The achieved accuracy is 84.23%, sensitivity 84.23%, specificity 97.37%, precision 84.7%, FPR 2.63%, F1 Score 84.17%, MCC 81.75%, and Kappa 53.59%.
引用
收藏
页码:6969 / 6976
页数:8
相关论文
共 50 条
  • [41] Lung nodule classification using deep feature fusion in chest radiography
    Wang, Changmiao
    Elazab, Ahmed
    Wu, Jianhuang
    Hu, Qingmao
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2017, 57 : 10 - 18
  • [42] Lyme rashes disease classification using deep feature fusion technique
    Ali, Ghulam
    Anwar, Muhammad
    Nauman, Muhammad
    Faheem, Muhammad
    Rashid, Javed
    SKIN RESEARCH AND TECHNOLOGY, 2023, 29 (11)
  • [43] Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images
    Mohan, Prakash
    Veerappampalayam Easwaramoorthy, Sathishkumar
    Subramani, Neelakandan
    Subramanian, Malliga
    Meckanzi, Sangeetha
    ELECTRONICS, 2022, 11 (24)
  • [44] Deep feature extraction based brain image classification model using preprocessed images: PDRNet
    Tasci, Burak
    Tasci, Irem
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 78
  • [45] Towards unbiased skin cancer classification using deep feature fusion
    Abdulredah, Ali Atshan
    Fadhel, Mohammed A.
    Alzubaidi, Laith
    Duan, Ye
    Kherallah, Monji
    Charfi, Faiza
    BMC MEDICAL INFORMATICS AND DECISION MAKING, 2025, 25 (01)
  • [46] Deep Learning Feature Fusion-Based Retina Image Classification
    Zhang Tianfu
    Zhong Shuncong
    Lian Chaoming
    Zhou Ning
    Xie Maosong
    LASER & OPTOELECTRONICS PROGRESS, 2020, 57 (24)
  • [47] Scene Classification Based on Two-Stage Deep Feature Fusion
    Liu, Yishu
    Liu, Yingbin
    Ding, Liwang
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (02) : 183 - 186
  • [48] GENDER CLASSIFICATION FROM FACE IMAGES USING MUTUAL INFORMATION AND FEATURE FUSION
    Perez, Claudio
    Tapia, Juan
    Estevez, Pablo
    Held, Claudio
    INTERNATIONAL JOURNAL OF OPTOMECHATRONICS, 2012, 6 (01) : 92 - 119
  • [49] Classification of recurrent depression using brain CT images through feature fusion
    Yang, Wenjun
    Xue, Lian
    Chen, Juan
    Wang, Yi
    Ding, Shizhen
    Zhang, Hao
    JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES, 2024, 17 (03)
  • [50] Fusion-Based Deep Learning Model for Hyperspectral Images Classification
    Kriti
    Haq, Mohd Anul
    Garg, Urvashi
    Khan, Mohd Abdul Rahim
    Rajinikanth, V
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 72 (01): : 939 - 957