Download Free Medical Lightweight Deep Learning on Smart Device for Early Detection of Skin Cancer PowerPoint Presentation

Lightweight Deep Learning on Smart Device for Early Detection of Skin Cancer PowerPoint Presentation

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Description : This medical presentation on cancer titled "Lightweight Deep Learning on Smart Device for Early Detection of Skin Cancer" tells us how smartphone and IoT devices based skin cancer detection system that utilizes deep learning and low-cost camera to take the snapshots of suspected skin lesions and distinguish between malignant and benign melanoma skin images.

Tags : Cancer, Deep Learning, Smart Device, Early Detection, Skin Cancer, IoT devices, skin lesions, malignant, benign melanoma skin images

Published on : May 01, 2021 | Views : 1766 | Downloads : 0

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Slide 1-

Dantong Yu Associate Professor School of Management New Jersey Institute Of Technology
Slide 2-

Uncontrolled growth of abnormal skin cells Often caused by ultraviolet radiation from sunshine or tanning beds Potential Genetic basis for susceptibility A major public health problem, with over 5 million newly diagnosed cases in the United States each year.
Slide 3-

A skin examination by a dermatologist is the way to get a definitive diagnosis of skin cancer. In many cases, the appearance alone is sufficient to make the diagnosis. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths each year.
Slide 4-

Malignant Samples Benign Samples
Slide 5-

We propose a smartphone and IoT devices based skin cancer detection system that utilizes deep learning and low-cost camera to take the snapshots of suspected skin lesions and distinguish between malignant and benign melanoma skin images.
Slide 6-

R. A. Novoa et. al., Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (Feb. 2017). Y. Li, A. Esteva*, R. Novoa, J. Ko, S. Thrun, Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning, NIPS Machine Learning for Healthcare Workshop 2016 Apple iTune and Google play SkinVision – Prevent, Detect and Track Skin Cancer
Slide 7-

In our method we use a variant of deep learning models called Convolutional Neural Networks (CNN) A typical CNN comprises of Convolutional, Pooling and Fully Connected layers
Slide 8-

Color (RGB) image One 4x4x3 filter (cube) The result of a single filter Another filter
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A first layer of filters A second layer of filters An MNIST image A third layer of filters (treat this as 1176-D vector) A “fully connected” layer (as before) Softmax probability of label
Slide 10-

Put Everything Together
Slide 11-

Training a Deep learning model from scratch is a time and resource consuming task. Not sufficient labeled data. To ameliorate this problem we adopt a Transfer Learning Based approach
Slide 12-

We adopt Inception Net, a deep Learning model trained on ImageNet dataset to classify natural images as our base model Training a model such as Inception Net can take even weeks on a high end GPU Therefore we fine-tuned Inception Net for the task of skin lesion classification into Malignant and Benign Melanoma Inception Net Architecture
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The Raspberry Pi 3 is single-board, low end computing device which has a quad-core Cortex-A53 processor with 1 GB RAM Can support camera as well as display devices Costs around $35 Supports Linux OS
Slide 15-

Train the model in a cluster of Raspberry PI in a distributed manner Used Tensorflow framework for training Between-graph replication and synchronous training for parameter updates.
Slide 16-

Experiment done on the dataset provided by the ISIC for the 2017 challenge on melanoma detection and classification. After the Transfer Learning step we were able to achieve an accuracy of 76% on a dataset of 2750 images with 521 malignant and 2229 benign images
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Makes the process cheap and readily available to masses No network connectivity required therefore can be deployed to remote and diverse regions without any problems Can be re-trained as per requirement on a low end device
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Movidius stick an another low end device specifically designed to speed up inference using a deep learning model In our experiments speed up of about 5 times was observed
Slide 19-

Training complex networks to improve accuracy. Integrate domain-knowledge, such as combining the “ABCDE” characteristics (Asymmetry , Border irregularity, Color, Diameter, Evolving size), SPIE 2018 Medical Image Enrich Dataset with GAN and InfoGan by embedding cancer sample into body and creating more realistic looking images and evolution history for training advanced network models.
Slide 20-

An Android application classifying skin lesion into Malignant or Benign