A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Abstract

Deep learning is a highly useful technique for the early identification of diabetes mellitus, according to the study done by numerous authors over the past few decades. By using pre-processing techniques on the dataset to get rid of various anomalies like over-fitting, under-fitting, redundancy, missing values, and non-significant features to make it more efficient for analysis, it is possible to increase the effectiveness of deep learning algorithms for diagnosing the disease. This work addresses the global problem of diabetes by exploring a revolutionary deep-learning method for early identification. Conventional convolutional neural network (CNN) models have drawbacks when used with numerical medical datasets, like this study's PIMA Indians Diabetes Database. The article suggests a technique for transforming numerical data into visual representations depending on feature relevance to get over this obstacle. This conversion makes it possible to use strong CNN models for diabetes early diagnosis. Classifying the created diabetic images after feeding them into CNN architectures that have already been trained on VGG16 and ResNet50. The promising outcomes with an accuracy of 97.19% demonstrate the possibility of the suggested strategy for improving diabetes detection and validating the effectiveness of diabetes imaging in obtaining an early diagnosis.