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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Published on: September 25, 2021

The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers.

Yasuhiro Fujisawa¹, Sae Inoue¹, Yoshiyuki Nakamura¹

¹Department of Dermatology, University of Tsukuba, Tsukuba, Japan.

Frontiers in Medicine

|September 12, 2019

Summary

Early detection of malignant skin tumors is crucial. Computer-aided diagnostics using deep learning show promise for classifying clinical images, potentially matching dermatologist accuracy and improving patient outcomes.

Keywords:

artificial intelligence clinical image convolutional neural network deep learning dermoscopy skin tumor classifier

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Skin tumor incidence is rising, with malignant types posing a lethal threat if diagnosis is delayed.
Expert dermatological screening is ideal but not practical for all patients.
Existing computer-aided diagnostic systems primarily focus on dermoscopic images, with fewer for conventional clinical images.

Purpose of the Study:

To review the development of computer-aided skin tumor classifiers.
To summarize the impact of deep learning technology on classification efficacy.
To explore the potential of deep learning in improving clinical image classification accuracy.

Main Methods:

Review of recent studies on computer-aided skin tumor diagnostics.
Focus on the application of deep learning technology for feature extraction and classification.
Analysis of systems classifying conventional clinical images.

Main Results:

Deep learning technology has significantly improved classification efficacy.
Potential exists to elevate computer classification accuracy of clinical images to dermatologist levels.
Fewer systems exist for classifying conventional clinical images compared to dermoscopic ones.

Conclusions:

Deep learning offers a promising approach to enhance computer-aided skin tumor detection from clinical images.
This technology could bridge the gap in accessibility to expert dermatological screening.
Further development is needed for robust clinical image classification systems.