# GPTNeXt: Biomedical Image Classification Investigations

## TL;DR

This paper introduces GPTNeXt, a new CNN model inspired by GPT, which achieves over 98% accuracy in classifying biomedical images of Alzheimer's, blood, and lung cancer.

## Contribution

The novel GPTNeXt CNN model and a deep feature engineering approach for biomedical image classification are introduced.

## Key findings

- GPTNeXt achieved over 98% classification accuracy on Alzheimer’s MR images.
- The model also showed high accuracy on blood and lung cancer image datasets.
- A lightweight CNN with strong performance was introduced.

## Abstract

Background/Objectives: In the field of computer vision, prominent solutions often rely on transformers and convolutional neural networks (CNNs). Researchers frequently incorporate CNNs and transformers in developing image classification models. This study aims to introduce an innovative CNN model inspired by the Generative Pretrained Transformer (GPT) architecture and assess its image classification capabilities. Methods: This study utilized three distinct biomedical image datasets to evaluate the efficacy of the proposed GPTNeXt model. The datasets encompassed (i) Alzheimer’s disease (AD) magnetic resonance (MR) images, (ii) blood images, and (iii) lung cancer images. The choice of these datasets aimed to showcase the GPTNeXt model’s versatile classification performance. The GPTNeXt model and a deep feature engineering approach based on it were developed. In this deep feature engineering model, features were extracted from the global average pooling layer of GPTNeXt, and a novel deep feature extraction method was employed. This method extracted features from the entire image and generated nine fixed-size patches. To identify the most informative features, iterative neighborhood component analysis (INCA) was applied. The classification phase involved three shallow classifiers to produce classification results. Results: The GPTNeXt-based feature engineering model was applied to the three aforementioned biomedical image datasets, achieving classification accuracies exceeding 98% for all of them. Conclusions: This study demonstrates the high effectiveness of the proposed approach, as evidenced by the exceptional classification performance on the selected biomedical image datasets. Additionally, a lightweight CNN was introduced, showcasing outstanding classification performance.