DoubleSENeXt: Investigations on Enchondroma Detection

Abstract

There are various deep learning models used to solve computer vision problems, with convolutional neural networks (CNNs) and transformers being commonly employed. However, these models have typically proposed by technological giants, and most researchers have relied on them. In this research, we aim to propose aAnew generation CNN model, termed Double Squeeze-and-Excitation Network (DoubleSENeXt), to address the stagnation in the development of new models. In this study, two new image classification models have been introduced: (i) DoubleSENeXt and (ii) an Exemplar Deep Feature Engineering (EDFE) model. The proposed DoubleSENeXt consists of four main stages:A(1) stem, (2) main, (3) downsampling, and (4) output stages. Additionally, we have presented a lightweight version of the proposed DoubleSENeXt. The EDFE model comprises three main phases: (i) feature extraction with the pretrained DoubleSENeXt, (ii) feature selection using Cumulative Weighted Iterative Neighborhood Component Analysis (CWINCA), and (iii) classification with the tkNN algorithm-based k-nearest neighbors. Both new models have been applied to a newly collected enchondroma image dataset for classification. Both models achieved over 92% test classification accuracy on this dataset, with the proposed DoubleSENeXt reaching 92.15% test classification accuracy, and the EDFE model further improving this accuracy to 97.67%.