Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network

2019 ◽  
Author(s):  
Guanxiong Cai ◽  
Yanhui Guo ◽  
Weiguo Chen ◽  
Hui Zeng ◽  
Yuanpin Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Computer Aided Detection ◽  
Full Field ◽  
Computer Aided ◽  
Deep Convolution Neural Network ◽  
Microcalcification Clusters

scholarly journals Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

2012 ◽  
Vol 03 (06) ◽  
pp. 1020-1028 ◽  
Author(s):  
Edén A. Alanís-Reyes ◽  
José L. Hernández-Cruz ◽  
Jesús S. Cepeda ◽  
Camila Castro ◽  
Hugo Terashima-Marín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Microcalcification Clusters

scholarly journals Gabor Feature Representation and Deep Convolution Neural Network for Marine Vessel Classification

2021 ◽  
Vol 8 (3) ◽  
pp. 121-126
Author(s):  
Hoang Long ◽  
Oh-Heum Kwon ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Neural Network ◽  
Surveillance System ◽  
Feature Representation ◽  
Convolution Neural Network ◽  
Sea State ◽  
Characteristics Method ◽  
Depressed State ◽  
Gabor Feature ◽  
Deep Convolution Neural Network

The Vessel Surveillance System (VSS), a crucial tool for fisheries monitoring, controlling, and surveillance, has been required to use for the reservation of the current depressed state of the world's fisheries by fisheries management agencies. An important issue in the vessel surveillance system is the classification of vessels. However, several factors, such as lighting, congestion, and sea state, will affect the vessel's appearance, making it more difficult to classify vessels. There are two main methods for conventional classifications of vessels: the traditional-based- characteristics method and the convolutional neural networks-used method. In this paper, we combine Gabor feature representation (GFR) and deep convolution neural network (DCNN) to classify vessels. Gabor filters in different directions and ratios are used to extract vessel characteristics to create a new image of vessels, which is DCNN's input. The visible and infrared spectrums (VAIS) dataset, the world's first publicly available dataset for paired infrared and visible vessel images, was used to validate the proposed method (GFR-DCNN). The numerical results showed that GFR-DCNN is more accurate than other methods.

scholarly journals Classification of skin cancer images using TensorFlow and inception v3

2018 ◽  
Vol 7 (2.7) ◽  
pp. 717 ◽  
Author(s):  
Bhavya Sai V ◽  
Narasimha Rao G ◽  
Ramya M ◽  
Sujana Sree Y ◽  
Anuradha T
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Cancer ◽  
Convolution Neural Network ◽  
Cancer Dataset ◽  
Machine Learning Classifiers ◽  
Human Eye ◽  
Deep Convolution Neural Network ◽  
Similar Appearance

It is easy for a human eye to distinguish the images of similar appearance but classifying the images like that of cancer affected skin  requires more expertise. And as the skin cancer cases are increasing globally, it requires more number of human experts. To overcome this problem, many people are working on constructing machine learning classifiers which can detect skin cancer automatically by    classifying skin images. This paper concentrates on developing an approach for predicting skin cancer by classifying images using deep convolution neural network. The proposed work is tested on standard cancer dataset and obtained more than 85% accuracy. 

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