scholarly journals Classification Of Skin Lesions By Topological Data Analysis Alongside With Neural Network

2020 ◽  
Author(s):  
Naiereh Elyasi ◽  
mehdi hosseini moghadam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Analysis ◽  
Skin Diseases ◽  
Persistent Homology ◽  
Skin Lesions ◽  
Topological Data Analysis ◽  
Deep Convolutional Neural Networks ◽  
Classification Result

In this paper we use TDA mapper alongside with deep convolutional neural networks in the classification of 7 major skin diseases. First we apply kepler mapper with neural network as one of its filter steps to classify the dataset HAM10000. Mapper visualizes the classification result by a simplicial complex, where neural network can not do this alone, but as a filter step neural network helps to classify data better. Furthermore we apply TDA mapper and persistent homology to understand the weights of layers of mobilenet network in different training epochs of HAM10000. Also we use persistent diagrams to visualize the results of analysis of layers of mobilenet network.

scholarly journals Classification Of Skin Lesions By Topological Data Analysis Alongside With Neural Network

2020 ◽  
Author(s):  
Naiereh Elyasi ◽  
mehdi hosseini moghadam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Analysis ◽  
Skin Diseases ◽  
Persistent Homology ◽  
Skin Lesions ◽  
Topological Data Analysis ◽  
Deep Convolutional Neural Networks ◽  
Classification Result

In this paper we use TDA mapper alongside with deep convolutional neural networks in the classification of 7 major skin diseases. First we apply kepler mapper with neural network as one of its filter steps to classify the dataset HAM10000. Mapper visualizes the classification result by a simplicial complex, where neural network can not do this alone, but as a filter step neural network helps to classify data better. Furthermore we apply TDA mapper and persistent homology to understand the weights of layers of mobilenet network in different training epochs of HAM10000. Also we use persistent diagrams to visualize the results of analysis of layers of mobilenet network.

Skin Cancer Classification with Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 1707-1713 ◽  
Author(s):  
Mingang Chen ◽  
Wenjie Chen ◽  
Wei Chen ◽  
Lizhi Cai ◽  
Gang Chai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Survival Rates ◽  
Skin Lesions ◽  
Classification Model ◽  
Deep Convolutional Neural Networks ◽  
Image Preprocessing ◽  
Skin Cancers

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

scholarly journals Deep convolutional neural networks for Raman spectrum recognition: a unified solution

The Analyst ◽  
2017 ◽  
Vol 142 (21) ◽  
pp. 4067-4074 ◽  
Author(s):  
Jinchao Liu ◽  
Margarita Osadchy ◽  
Lorna Ashton ◽  
Michael Foster ◽  
Christopher J. Solomon ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Raman Spectrum ◽  
Convolutional Neural Network ◽  
Raman Spectra ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks

Classification of unprocessed Raman spectra using a convolutional neural network.

scholarly journals Effect of image noise on the classification of skin lesions using deep convolutional neural networks

2020 ◽  
Vol 25 (3) ◽  
pp. 425-434 ◽  
Author(s):  
Xiaoyu Fan ◽  
Muzhi Dai ◽  
Chenxi Liu ◽  
Fan Wu ◽  
Xiangda Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Image Noise ◽  
Deep Convolutional Neural Networks

scholarly journals A Comparison Study of Deep Convolutional Neural Networks for the Classification of Brazilian Savannah Pollen Grains: Preliminary Results

2019 ◽  
Author(s):  
Bruno Aristimunha ◽  
Felipe Silveira Brito Borges ◽  
Ariadne Barbosa Gonçalves ◽  
Hemerson Pistori
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pollen Grains ◽  
Pollen Grain ◽  
Comparison Study ◽  
Deep Convolutional Neural Networks ◽  
Classification Result ◽  
Brazilian Savannah ◽  
Accuracy Rates

The classification of pollen grains images are currently done manually and visually, being a weariful task and predisposed to mistakes due to human exhaustion. In this paper, the authors introduce an automatic classification of 55 different pollen grain classes, using convolutional neural networks. Different architectures and hyperparameters were used to improve the classification result. Using the networks VGG16, VGG19, and InceptionV3, were obtained accuracy rates over 93.58%.

scholarly journals Topological image modification for object detection and topological image processing of skin lesions

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Robin Vandaele ◽  
Guillaume Adrien Nervo ◽  
Olivier Gevaert
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Object Detection ◽  
Persistent Homology ◽  
Skin Lesions ◽  
Topological Data Analysis ◽  
Topological Properties ◽  
Unsupervised Segmentation ◽  
Noisy Background

AbstractWe propose a new method based on Topological Data Analysis (TDA) consisting of Topological Image Modification (TIM) and Topological Image Processing (TIP) for object detection. Through this newly introduced method, we artificially destruct irrelevant objects, and construct new objects with known topological properties in irrelevant regions of an image. This ensures that we are able to identify the important objects in relevant regions of the image. We do this by means of persistent homology, which allows us to simultaneously select appropriate thresholds, as well as the objects corresponding to these thresholds, and separate them from the noisy background of an image. This leads to a new image, processed in a completely unsupervised manner, from which one may more efficiently extract important objects. We demonstrate the usefulness of this proposed method for topological image processing through a case-study of unsupervised segmentation of the ISIC 2018 skin lesion images. Code for this project is available on https://bitbucket.org/ghentdatascience/topimgprocess.

scholarly journals Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

2021 ◽  
Vol 2128 (1) ◽  
pp. 012013
Author(s):  
Laila Moataz ◽  
Gouda I. Salama ◽  
Mohamed H. Abd Elazeem
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Classification Accuracy ◽  
Skin Lesions ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Proposed Model ◽  
Prior Models ◽  
Cancer Diseases

Abstract Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

scholarly journals The Topology of Neuronal Structures Exposed to Cosmic Radiation

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Analysis ◽  
Cosmic Radiation ◽  
Network Code ◽  
Topological Data Analysis ◽  
Analysis Approach ◽  
The Neural Networks ◽  
Topological Data

In this paper, we focus on some leader NASA experiences to explore how cosmic radiation caused significant reductions in dendrite and spine complexity. We adopt a topological data analysis approach and extract more information then the classical methods. Our key idea is to use the NASA images of the neural networks of some mouses that were exposed 12 weeks to cosmic radiation. We associate to this neural network code bares that give us more information, that that given by the original experiences.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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