Bilinear-Convolutional Neural Network Using a Matrix Similarity-based Joint Loss Function for Skin Disease Classification

• URL: http://arxiv.org/abs/2406.00696v1
• Date: Sun, 2 Jun 2024 10:19:02 GMT
• Title: Bilinear-Convolutional Neural Network Using a Matrix Similarity-based Joint Loss Function for Skin Disease Classification
• Authors: Belal Ahmad, Mohd Usama, Tanvir Ahmad, Adnan Saeed, Shabnam Khatoon, Long Hu,
• Abstract summary: We propose a model for skin disease classification using a Bilinear Convolutional Neural Network (BCNN) with a Constrained Triplet Network (CTN) BCNN can capture rich spatial interactions between features in image data. The proposed model is trained to extract the intra-class features from a deep network.
• Score: 3.710922682020501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we proposed a model for skin disease classification using a Bilinear Convolutional Neural Network (BCNN) with a Constrained Triplet Network (CTN). BCNN can capture rich spatial interactions between features in image data. This computes the outer product of feature vectors from two different CNNs by a bilinear pooling. The resulting features encode second-order statistics, enabling the network to capture more complex relationships between different channels and spatial locations. The CTN employs the Triplet Loss Function (TLF) by using a new loss layer that is added at the end of the architecture called the Constrained Triplet Loss (CTL) layer. This is done to obtain two significant learning objectives: inter-class categorization and intra-class concentration with their deep features as often as possible, which can be effective for skin disease classification. The proposed model is trained to extract the intra-class features from a deep network and accordingly increases the distance between these features, improving the model's performance. The model achieved a mean accuracy of 93.72%.

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