Deep learning pipeline for image classification on mobile phones

by   Muhammad Muneeb, et al.

This article proposes and documents a machine-learning framework and tutorial for classifying images using mobile phones. Compared to computers, the performance of deep learning model performance degrades when deployed on a mobile phone and requires a systematic approach to find a model that performs optimally on both computers and mobile phones. By following the proposed pipeline, which consists of various computational tools, simple procedural recipes, and technical considerations, one can bring the power of deep learning medical image classification to mobile devices, potentially unlocking new domains of applications. The pipeline is demonstrated on four different publicly available datasets: COVID X-rays, COVID CT scans, leaves, and colorectal cancer. We used two application development frameworks: TensorFlow Lite (real-time testing) and Flutter (digital image testing) to test the proposed pipeline. We found that transferring deep learning models to a mobile phone is limited by hardware and classification accuracy drops. To address this issue, we proposed this pipeline to find an optimized model for mobile phones. Finally, we discuss additional applications and computational concerns related to deploying deep-learning models on phones, including real-time analysis and image preprocessing. We believe the associated documentation and code can help physicians and medical experts develop medical image classification applications for distribution.


page 8

page 9

page 13

page 17

page 18


Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

Deep learning models have gained remarkable performance on a variety of ...

HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach

Image classification is central to the big data revolution in medicine. ...

An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks

Novel and high-performance medical image classification pipelines are he...

UX Heuristics and Checklist for Deep Learning powered Mobile Applications with Image Classification

Advances in mobile applications providing image classification enabled b...

TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networks

Deep learning models have been developed for a variety of tasks and are ...

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

Deep learning methods have emerged as powerful tools for analyzing histo...

Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma

Background: Margin assessment of basal cell carcinoma using the frozen s...

Please sign up or login with your details

Forgot password? Click here to reset