INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses

TL;DR

This paper introduces InceptNet, a deep neural network designed for early disease detection in medical images, combining Inception modules with U-Net architecture to improve accuracy and efficiency across various medical imaging tasks.

Contribution

The study proposes InceptNet, a novel deep neural network that enhances medical image segmentation accuracy and speed by integrating Inception modules with U-Net, and evaluates user interaction with the application.

Findings

InceptNet outperforms previous models on four benchmark datasets.

Accuracy improvements ranged from 0.9531 to 0.9945 across datasets.

User interaction assessments show positive engagement with the application.

Abstract

In view of the recent paradigm shift in deep AI based image processing methods, medical image processing has advanced considerably. In this study, we propose a novel deep neural network (DNN), entitled InceptNet, in the scope of medical image processing, for early disease detection and segmentation of medical images in order to enhance precision and performance. We also investigate the interaction of users with the InceptNet application to present a comprehensive application including the background processes, and foreground interactions with users. Fast InceptNet is shaped by the prominent Unet architecture, and it seizes the power of an Inception module to be fast and cost effective while aiming to approximate an optimal local sparse structure. Adding Inception modules with various parallel kernel sizes can improve the network's ability to capture the variations in the scaled regions of interest. To experiment, the model is tested on four benchmark datasets, including retina blood vessel segmentation, lung nodule segmentation, skin lesion segmentation, and breast cancer cell detection. The improvement was more significant on images with small scale structures. The proposed method improved the accuracy from 0.9531, 0.8900, 0.9872, and 0.9881 to 0.9555, 0.9510, 0.9945, and 0.9945 on the mentioned datasets, respectively, which show outperforming of the proposed method over the previous works. Furthermore, by exploring the procedure from start to end, individuals who have utilized a trial edition of InceptNet, in the form of a complete application, are presented with thirteen multiple choice questions in order to assess the proposed method. The outcomes are evaluated through the means of Human Computer Interaction.