Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images

If this is to be REF-eligible it needs a full text file attached, this cannot be final published pdf but could be version after review but before publisher formatting applied researcher confirmed item not refabable so happy to process without file 13/5/20

Recent advances in sensor fault diagnosis: a review

Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

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A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques.

Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84-86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67-0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.

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A smart tele-cytology point-of-care platform for oral cancer screening.

Transmission and distribution lines are vital links between generating units and consumers. They are exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be immediately taken care of in order to minimize damage caused by it. This paper focuses on detecting the faults on electric power transmission lines using artificial neural networks. A feed forward neural network is employed, which is trained with back propagation algorithm. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. The developed neural network is capable of detecting single line to ground and double line to ground for all the three phases. Simulation is done using MATLAB Simulink to demonstrate that artificial neural network based method are efficient in detecting faults on transmission lines and achieve satisfactory performances. A 300km, 25kv transmission line is used to validate the proposed fault detection system. Hardware implementation of neural network is done on TMS320C6713. Keywords: Transmission Line, Asymmetric fault detection, Artificial neural network (ANN), Back Propagation algorithm, DSP processor TMS320C6713, Code Composer Studio, MATLAB/Simulink

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Intelligent Fault Identification System for Transmission Lines Using Artificial Neural Network

Abstract This article deals with detection and accommodation of sensor faults in longitudinal dynamics of an F8 aircraft model. Both the detection of the fault and reconfiguration of the failed sensor are done with the help of neural network-based models. Detection of a sensor fault is done with the help of knowledge-based neural network fault detection (KBNNFD). Apart from KBNNFD, another neural network model is developed in this article for the reconfiguration of the failed sensor. A model-based approach of the neural network (MBNN) is developed, which uses the radial basis function of the neural network. MBNN successfully does the task of providing analytical redundancy for the aircraft sensor. In this work, both detection and reconfiguration of a fault is done using neural networks. Hence, the control system becomes robust for handling sensor failures near steady state and reconfiguration is also faster. A generalized regression neural network (GRNN), which is a type of radial basis network, is used for MBNN, which gives very efficient results for function approximation. An F8 aircraft model and C-Star controller, which improves its handling quality, are used for validation of the method involved. Models of F8 aircraft, C-Star controller, KBNNFD, and MBNN were developed using MATLAB/Simulink. Successful implementation and simulation results are shown and discussed using Simulink.

Neural Network-Based Sensor Fault Accommodation in Flight Control System

Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology images that are used for discriminating various stages of cervical cancer. The dominant features used for diagnosis are Nucleus to cytoplasm ratio, shape, and color intensity along with nucleus area, perimeter and eccentricity. These features are used to train the neural network using Back-propagation algorithm of supervised training method. The cytology cells were then successfully classified as non-cancerous, lowgrade and high-grade cancer cells. Neural Network Based Automated System for Diagnosis of Cervical Cancer

Neural Network Based Automated System for Diagnosis of Cervical Cancer

fault in aircraft is detected based on two different approaches. The first approach, well documented in literature, is based on algorithmic method dealing with Luenberger observers. The second approach, which is followed in this paper, is based on Knowledge based neural network fault detection (KBNNFD). KBNNFD uses gradient descent back propagation training algorithm of neural network. A C-Star controller of F8 aircraft model, which improves the handling qualities, is used for validation of the KBNNFD. Neural network is trained with certain features of F8 aircraft model and C-Star controller enabling it to detect the faulty sensor.A comparative analysis of both the methods is done for various cases of stuck fault of the sensor for flight control system. Nz (Normal acceleration) sensor failure was considered because of its importance in C-Star controller. Knowledge-based approach of neural network, used in this work, has come out with results indicating that it takes less time to detect the faulty Nz sensor during transition, steady state and also in the presence of random noise. Results show improvement when compared to algorithmic methods with regard to the time taken to detect faults and ability to detect sensor faults especially near steady state. Investigations have been carried out using Matlab and Simulink.

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Neural Network based Sensor Fault Detection for Flight Control Systems

This work presents the implementation of a trained Artificial Neural Network (ANN) for a certain application. A Multi Layer Perceptron (MLP) has been synthesized and implemented on Spartan3 FPGA. The weight matrices and bias has been provided by separate simulation software like MATLAB/Simulink. The implemented network has been verified in Xilinx ISE using Verilog programming language. The device utilization summary illustrates that the implemented perceptron utilizes few slices on FPGA which makes it suitable for large scale implementation. The implementation of FPGA based neural network is verified for a specific application. Keywords- Artificial Neural Network, FPGA implementation, Multilayer Perceptron(MLP), Verilog.

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Seema Singh

Papers

Intelligent Fault Identification System for Transmission Lines Using Artificial Neural Network

Neural Network-Based Sensor Fault Accommodation in Flight Control System

Neural Network Based Automated System for Diagnosis of Cervical Cancer

Neural Network based Sensor Fault Detection for Flight Control Systems

FPGA Implementation of a Trained Neural Network