Skin Cancer Classification using Convolutional Neural Networks: Systematic Review (Preprint)

BACKGROUND State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable life-saving and fast diagnoses, even outside the hospital via installation on mobile devices. To our knowledge, at present, there is no review of the current work in this research area. OBJECTIVE This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. METHODS We searched the Google Scholar, PubMed, Medline, Science Direct, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. RESULTS We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large data set and then optimize its parameters to the classification of skin lesions are both the most common methods as well as display the best performance with the currently available limited data sets. CONCLUSIONS CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use non-public data sets for training and/or testing, thereby making reproducibility difficult.

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AN OVERVIEW OF CLASSIFICATION METHODS FROM DERMOSCOPY IMAGES IN SKIN LESION DIAGNOSTIC

Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska ◽

10.35784/iapgos.1569 ◽

2020 ◽

Vol 10 (2) ◽

pp. 36-39

Author(s):

Magdalena Michalska ◽

Oksana Boyko

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Skin Lesion ◽

Human Skin ◽

Random Forests ◽

Skin Lesions ◽

Data Sets ◽

Classification Methods ◽

Knn Classifier ◽

Dermatological Disease

The article contains a review of selected classification methods of dermatoscopic images with human skin lesions, taking into account various stages of dermatological disease. The described algorithms are widely used in the diagnosis of skin lesions, such as artificial neural networks (CNN, DCNN), random forests, SVM, kNN classifier, AdaBoost MC and their modifications. The effectiveness, specificity and accuracy of classifications based on the same data sets were also compared and analyzed.

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Preprocessing for Enhancing the Classification of Pulmonary Data Sets using Convolutional Neural Networks

PROGRAMMNAYA INGENERIA ◽

10.17587/prin.9.369-374 ◽

2018 ◽

Vol 9 (10) ◽

pp. 369-374

Author(s):

N. Esmaeilishahmirzadi ◽

◽

H. Mortezapour ◽

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Data Sets

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Skin Cancer Classification with Deep Convolutional Neural Networks

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3078 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1707-1713 ◽

Cited By ~ 1

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.

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Multi-Branch-CNN: classification of ion channel interacting peptides using parallel convolutional neural networks

10.1101/2021.11.13.468342 ◽

2021 ◽

Author(s):

Jielu Yan ◽

Bob Zhang ◽

Mingliang Zhou ◽

Hang Fai Kwok ◽

Shirley W.I. Siu

Keyword(s):

Neural Networks ◽

Ion Channels ◽

Ion Channel ◽

Convolutional Neural Networks ◽

Data Sets ◽

The Novel ◽

Sodium Potassium ◽

Test Set ◽

Drug Candidates

Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. There are several studies to identify ion channel interacting peptides computationally, but, to the best of our knowledge, none of them published available tools for prediction. To provide a solution, we present Multi-branch-CNN, a parallel convolutional neural networks (CNNs) method for identifying three types of ion channel peptide binders (sodium, potassium, and calcium). Our experiment shows that the Multi-Branch-CNN method performs comparably to thirteen traditional ML algorithms (TML13) on the test sets of three ion channels. To evaluate the predictive power of our method with respect to novel sequences, as is the case in real-world applications, we created an additional test set for each ion channel, called the novel-test set, which has little or no similarities to the sequences in either the sequences of the train set or the test set. In the novel-test experiment, Multi-Branch-CNN performs significantly better than TML13, showing an improvement in accuracy of 6%, 14%, and 15% for sodium, potassium, and calcium channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). To facilitate applications, the data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.

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Modified Deep Neural Networks for Dog Breeds Identification

10.20944/preprints201812.0232.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aydin Ayanzadeh ◽

Sahand Vahidnia

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

State Of The Art ◽

The State ◽

Fine Tuning ◽

Test Accuracy ◽

Data Sets ◽

Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

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FruitVegCNN: Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC

10.36227/techrxiv.12686051.v1 ◽

2020 ◽

Author(s):

Somdip Dey ◽

Suman Saha ◽

Amit Singh ◽

Klaus D. Mcdonald-Maier

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Power Efficiency ◽

Fruits And Vegetables ◽

State Of The Art ◽

Agricultural Industry ◽

Power Efficient ◽

System On A Chip ◽

Memory Efficient

<div><div><div><p>Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.</p></div></div></div>

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HierarchyNet: Hierarchical CNN-Based Urban Building Classification

Remote Sensing ◽

10.3390/rs12223794 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3794

Author(s):

Salma Taoufiq ◽

Balázs Nagy ◽

Csaba Benedek

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

State Of The Art ◽

Feedback Signal ◽

Hierarchical Network ◽

Urban Buildings ◽

Virtual City ◽

Classification Tasks ◽

Coarse To Fine

Automatic building categorization and analysis are particularly relevant for smart city applications and cultural heritage programs. Taking a picture of the facade of a building and instantly obtaining information about it can enable the automation of processes in urban planning, virtual city tours, and digital archiving of cultural artifacts. In this paper, we go beyond traditional convolutional neural networks (CNNs) for image classification and propose the HierarchyNet: a new hierarchical network for the classification of urban buildings from all across the globe into different main and subcategories from images of their facades. We introduce a coarse-to-fine hierarchy on the dataset and the model learns to simultaneously extract features and classify across both levels of hierarchy. We propose a new multiplicative layer, which is able to improve the accuracy of the finer prediction by considering the feedback signal of the coarse layers. We have quantitatively evaluated the proposed approach both on our proposed building datasets, as well as on various benchmark databases to demonstrate that the model is able to efficiently learn hierarchical information. The HierarchyNet model is able to outperform the state-of-the-art convolutional neural networks in urban building classification as well as in other multi-label classification tasks while using significantly fewer parameters.

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Artist, Style and Year Classification using Face Recognition and Clustering with Convolutional Neural Networks

10.5121/csit.2020.101604 ◽

2020 ◽

Author(s):

Doruk Pancaroglu

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Face Recognition ◽

Image Classification ◽

Convolutional Neural Networks ◽

Performance Metrics ◽

Fine Art ◽

Classification Methods

Artist, year and style classification of fine-art paintings are generally achieved using standard image classification methods, image segmentation, or more recently, convolutional neural networks (CNNs). This works aims to use newly developed face recognition methods such as FaceNet that use CNNs to cluster fine-art paintings using the extracted faces in the paintings, which are found abundantly. A dataset consisting of over 80,000 paintings from over 1000 artists is chosen, and three separate face recognition and clustering tasks are performed. The produced clusters are analyzed by the file names of the paintings and the clusters are named by their majority artist, year range, and style. The clusters are further analyzed and their performance metrics are calculated. The study shows promising results as the artist, year, and styles are clustered with an accuracy of 58.8, 63.7, and 81.3 percent, while the clusters have an average purity of 63.1, 72.4, and 85.9 percent.

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