CMSFL-Net: Consecutive Multi-scale Feature Learning-based Image Classification Model

Abstract

Extracting useful features at multiple scales is one of the most crucial tasks in computer vision. Emergence of deep learning(DL) techniques and progress of convolutional neural networks (CNNs) allow powerful multi-scale feature extraction that result in stable performance increase in numerous real-life applications. However, currently available state-of-the-art powerful methods primarily rely on parallel multi-scale feature extraction approach and despite providing competitive accuracy, the models lead to poor results in efficient computation and lack at generalizing on small-scale images. On the other hand, efficient and lightweight networks cannot learn useful features properly and result in underfitting problem in case of training with small-scale images or datasets with limited number of samples. Considering these problems, we proposed a novel image classification system based on elaborate data pre-processing step and carefully designed CNN model architecture. Specifically, consecutive multiscale feature learning network (CMSFL-Net) employs consecutive feature learning approach based on usage of various feature maps with different receptive fields to obtain faster training/inference and higher accuracy. In the conducted experiments using 6 real-life image classification datasets, including small-scale, large-scale, and limited data, the proposed system obtained comparable accuracy when compared with the existing state-of-the-art powerful and efficient networks by outperforming them in terms of efficiency and speed. Moreover, the proposed system achieved the best results in accuracy-efficiency tradeoff in comparison with the currently available models.

A customized deep learning framework for skin lesion classification using dermoscopic images

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