Optimizing Tumor Classification Through Transfer Learning and Particle Swarm Optimization-Driven Feature Extraction

Abstract

Brain tumors pose a significant threat, especially when not detected early. The Inception v3 machine learning model has found extensive applications in computer vision and related fields. This study aims to develop a robust transfer learning model for classification, adaptable to various data modalities through neural networks. However, the training process for these neural networks is complex, being both demanding and computationally intensive. To tackle this challenge, we introduce an innovative training approach for Inception v3 referred to as ‘PSO-INCEPT’ (Particle Swarm Optimization-based Inception v3 training). In this method, the weight vectors for each Inception v3 model are analogized to particle positions in a phase space. The PSO cooperates with the ADAM optimizer in achieving the purpose of training with the best performance and generalization. This research is composed of two main parts, the first stage is being performed by the model independently using the ADAM optimizer. In the following stage, PSO-INCEPT models share the latest weight vectors or particle coordinates and loss function approximations via training. The optimization function then uses them to improve the validation accuracy. The effectiveness of PSO-INCEPT was evaluated through experiments that were conducted on Kaggle datasets that provide a common base ground by having four distinct classes. Experimental studies have proven the extraordinary ability of the proposed model by providing 99.33% validation accuracy and 99.95% training accuracy which shows exceptional performance.

Keywords

• Deep learning
• healthcare technology
• transfer learning
• brain tumor classification
• convolutional neural network (CNN)