Classification of Space Targets with Micro-motion Based on Deep CNN

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

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Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

Brain Sciences ◽

10.3390/brainsci11030352 ◽

2021 ◽

Vol 11 (3) ◽

pp. 352

Author(s):

Isselmou Abd El Kader ◽

Guizhi Xu ◽

Zhang Shuai ◽

Sani Saminu ◽

Imran Javaid ◽

...

Keyword(s):

Neural Network ◽

Magnetic Resonance Imaging ◽

Brain Tumor ◽

Magnetic Resonance ◽

Brain Tumors ◽

Medical Image ◽

Resonance Imaging ◽

Feature Maps ◽

Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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Micro-motion Classification of Rotor UAV and Flying Bird via CNN and FMCW Radar

10.1007/978-3-030-90196-7_5 ◽

2021 ◽

pp. 55-67

Author(s):

Xiaolong Chen ◽

Jian Guan ◽

Jiefang Li ◽

Weishi Chen

Keyword(s):

Motion Classification ◽

Fmcw Radar ◽

Micro Motion

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Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.2982064 ◽

2020 ◽

Vol 58 (10) ◽

pp. 7355-7370 ◽

Cited By ~ 4

Author(s):

Xudong Zhao ◽

Ran Tao ◽

Wei Li ◽

Heng-Chao Li ◽

Qian Du ◽

...

Keyword(s):

Random Walk ◽

Lidar Data ◽

Deep Cnn ◽

Joint Classification

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Performance Measures of Respiratory Disease Classification using Deep Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35109 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 884-892

Author(s):

G. Rama Janani

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Performance Measures ◽

Respiratory Infections ◽

Respiratory Illness ◽

Extraction Methods ◽

Disease Classification ◽

Deep Cnn

The paper is based on classification of respiratory illness like covid 19 and pneumonia by using deep learning. The symptoms of COVID-19 and pneumonia are similar. Due to this, it is often difficult to identify what is causing your condition without being tested for COVID-19 or other respiratory infections. To find out how COVID-19 and pneumonia differs from one another, this paper presents that a novel Convolutional Neural Network in Tensor Flow and Keras based Covid-19 pneumonia classification. The proposed system supported implements CNN using Pneumonia images to classify the Covid-19, normal, pneumonia. The knowledge from these studies can potentially help in diagnosis of the concerned disease. It is predicted that the success of the anticipated results will increase if the CNN method is supported by adding extra feature extraction methods for classifying covid-19 and pneumonia successfully thereby improving the efficacy and potential of using deep CNN to pictures.

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