Session 14 - Healthcare: A Dense-Inception Network for Medical Image Classification
Presentation Type
Oral
Student
Yes
Track
Health Care Application
Abstract
Recently, the advent of deep convolutional neural network (DCNN) have led to impressive accomplishments on image recognition tasks which was preceded by the breakthrough performance of Inception network for solving image and recognition tasks. In healthcare economies, deep learning approaches have shown great promise in medical diagnosis and treatment. In this work, we proposed a new model architecture that integrates DenseBlocks into the Inception Module that we refer to as DINET. The novel model captures multi-scale learned features while increasing parameter efficiency throughout the network. Our aim is to investigate the impact of maintaining a balanced tradeoff between model efficiency and effectiveness of preventing the problem of vanishing gradient. For our medical images classification problem, specifically using Chest x-ray images, experiment results using our DINET model shows modest performance improvements. In our future research directions, other versions of DINET modules will be experimented to shed more light on the impact of strategically positioning the DINET modules in the network to achieve the trade-offs of model efficiency and performance while increasing interaction of multi-scale learned features.
Key words: Chest X-rays, DCNN, Deep learning, DINET, healthcare, medical images.
Start Date
2-11-2020 3:30 PM
End Date
2-11-2020 4:30 PM
Session 14 - Healthcare: A Dense-Inception Network for Medical Image Classification
Campanile & Hobo Day Gallery (A & B)
Recently, the advent of deep convolutional neural network (DCNN) have led to impressive accomplishments on image recognition tasks which was preceded by the breakthrough performance of Inception network for solving image and recognition tasks. In healthcare economies, deep learning approaches have shown great promise in medical diagnosis and treatment. In this work, we proposed a new model architecture that integrates DenseBlocks into the Inception Module that we refer to as DINET. The novel model captures multi-scale learned features while increasing parameter efficiency throughout the network. Our aim is to investigate the impact of maintaining a balanced tradeoff between model efficiency and effectiveness of preventing the problem of vanishing gradient. For our medical images classification problem, specifically using Chest x-ray images, experiment results using our DINET model shows modest performance improvements. In our future research directions, other versions of DINET modules will be experimented to shed more light on the impact of strategically positioning the DINET modules in the network to achieve the trade-offs of model efficiency and performance while increasing interaction of multi-scale learned features.
Key words: Chest X-rays, DCNN, Deep learning, DINET, healthcare, medical images.