Framework for psoriasis/molluscum detection in skin images using ResNetV2 variants

Background

Computer algorithms are extensively utilized in hospitals for the purpose of achieving expedited and precise disease identification from medical data. The objective of this study is to propose a deep-learning (DL) framework to classify the chosen digital skin image (DSI) database into psoriasis and molluscum with better accuracy.

Aims and objectives

Aims to develop a DL-tool using the pre-trained ResNetV2 DL-models and verify the performance of the developed tool using a chosen DSI database. Further, confirm the merit of the ResNetV2-based tool against other chosen DL-models.

Methods and results

This study initially examines the performance of chosen pre-trained DL (PDL) methods using the DSI database using conventional and fused features. Proposed DL-tool consist the following stages; (i) image collection and resizing it 224x224x3 pixels, (ii) deep-features extraction using the selected PDL, (iii) feature reduction and serial features concatenation to get a new features vector, and (iv) the performance evaluation through three-fold cross validation and confirmation. The feature reduction and serial features fusion of this study is performed initially using feature sorting based on its rank and 50% dropout and Particle Swarm Optimization (PSO) based feature reduction. The reduced features of two chosen PDL-models are then considered to obtain the Fused Deep Features (FDF) and the classification task is executed on the DSI data to verify the performance of the developed DL-tool. The experimental outcome of this study confirms that the proposed scheme helps to provide a detection accuracy of >97%, when the K-Nearest Neighbour based classification is executed.

Conclusion

Investigational outcome of this study confirms that the proposed DL-tool helps to achieve better detection accuracy when the features of ResNetV2 models are considered to generate the FDF. Further, the accuracy achieved with the PSO based FDF is better compared to the conventional method generated FDF.