Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning

In a cognitive radio network (CRN), spectrum sensing is an important prerequisite for improving the utilization of spectrum resources. In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. Firstly, we analyze the cyclic autocorrelation of an OFDM signal and the cyclic spectrum obtained by the time domain smoothing fast Fourier transformation (FFT) accumulation algorithm (FAM), and the cyclic spectrum is normalized to gray scale processing to form a cyclic autocorrelation gray scale image. Then, we learn the deep features of layer-by-layer extraction by the improved CNN classic LeNet-5 model. Finally, we input the test set to verify the trained CNN model. Simulation experiments show that this method can complete the spectrum sensing task by taking advantage of the cycle spectrum, which has better spectrum sensing performance for OFDM signals under a low signal-noise ratio (SNR) than traditional methods.

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Crop identification and disease classification using traditional machine learning and deep learning approaches

Journal of Engineering Research ◽

10.36909/jer.11941 ◽

2021 ◽

Author(s):

Aravind Krishnaswamy Rangarajan ◽

◽

Raja Purushothaman ◽

Maheswari Prabhakar ◽

Cezary Szczepański ◽

...

Keyword(s):

Deep Learning ◽

Health Status ◽

Correlation Energy ◽

Learning Algorithm ◽

Disease Classification ◽

Support Vector ◽

Leaf Spot Disease ◽

Learning Approaches ◽

Gray Scale ◽

Cercospora Leaf Spot

Crop and disease classification is one of the important problems in automation of agricultural processes with multi-cropping method where the field is cultivated with more than one crop. In order to solve this classification problem, a study has been carried out in the field cultivating eggplant (Solanum melongena) and tomato (Solanum lycopersicum) using the images obtained from a mobile phone camera. Textural descriptors namely contrast, correlation, energy and homogeneity were extracted from the gray-scale converted RGB image for crop identification, i.e., (tomato or eggplant) and the same descriptors were extracted from the gray-scale converted image from Hue Saturation Value (HSV) for disease classification (due to Cercospora leaf spot disease or two-spotted spider infestation). Discriminant analysis, Naive Bayes algorithm, support vector machine and neural network were the classification algorithms used with a resulting best accuracy of 97.61%, 95.62%, 98.01% and 98.94% for crop identification, 86.09%, 76.52%, 86.96% and 86.04% for disease classification respectively. Similarly, application of algorithm with 6 histogram-based descriptors for health status detection resulted in an accuracy of 66.67%, 37.04%, 50% and 72.9% respectively. Deep learning algorithm namely AlexNet was also evaluated which resulted in an accuracy of 100% for crop identification, 89.36% for health status detection and 81.51% for disease classification. Among the algorithms, AlexNet resulted in the best average accuracy of 90.29% for the above classification tasks.

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Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

Journal of Healthcare Engineering ◽

10.1155/2021/4109102 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Benzhen Guo ◽

Yanli Ma ◽

Jingjing Yang ◽

Zhihui Wang

Keyword(s):

Deep Learning ◽

Health Status ◽

Internet Of Things ◽

Healthcare System ◽

Learning Model ◽

Gray Scale ◽

Data Set ◽

Smart Healthcare ◽

Cloud Server ◽

Deep Learning Model

Introduction. Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward. Methods. Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set. Results. Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%. Conclusion. The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.

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Classification of Image using Convolutional Neural Networks

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37581 ◽

2021 ◽

Vol 9 (8) ◽

pp. 1212-1214

Author(s):

Dr. Abhay E Wagh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Bench Mark ◽

Exact Order ◽

Gray Scale ◽

Data Set

Abstract: Now a day, with the rapid advancement in the digital contents identification, auto classification of the images is most challenging job in the computer field. Programmed comprehension and breaking down of pictures by framework is troublesome when contrasted with human visions. A Several research have been done to defeat issue in existing classification system,, yet the yield was limited distinctly to low even out picture natives. Nonetheless, those approach need with exact order of pictures. This system uses deep learning algorithm concept to achieve the desired results in this area like computer. Our framework presents Convolutional Neural Network (CNN), a machine learning algorithm is used for automatic classification the images. This system uses the Digit of MNIST data set as a bench mark for classification of gray-scale images. The gray-scale images are used for training which requires more computational power for classification of those images. Using CNN network the result is near about 98% accuracy. Our model accomplishes the high precision in grouping of images. Keywords: Convolutional Neural Network (CNN), deep learning, MINIST, Machine Learning.

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