Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Akkar, Hanan A. R.; Haddad, Suhad Qasim G.

doi:10.30684/etj.v38i3b.1666

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…Enhancement images were applied and considered necessary to improve the quality of images [4], [11]. To enhance CT scan imaging efficiency and obtain an accurate diagnosis.…”

Section: Proposed Methodsmentioning

confidence: 99%

Medical Image Enhancement Techniques

2022

IJCCCE

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Computed tomography (CT) is used to diagnose diseases and tumors. A special dye called contrast material is used in CT scans to assist emphasize the parts of the body being examined. Therefore, an enhancement technique to improve CT images' degradation is needed. This paper aims to present a method to enhance the quality of Ct-Scan images based on discrete wavelets transform and the Retinex algorithm. The proposed methods are based on the Retinex algorithm parameters or Dark Channel Prior algorithm parameters, according to the output image from preprocessing and discrete wavelets algorithm steps to increase the lightness degree of an image, remove possible noise from the image improve the contrast. The results of experiments of the enhanced image outputted from the Retinex model compared with one outputted from the Dark Channel Prior method. Hence, image quality based on the DCP method is a higher degree of enhanced rate and reasonable rate of noise removal-based enhancement measures, which were SI, MSE, IQI, SNR, and SNR, but was very attentive to the percentage values of IQI and SI. So the DCP with WT was recorded as the highest rate of enhancement. Index Terms— Dark Channel Prior, Image quality assessment, Liver CT, Retinex model, Wavelet Transform.

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“…Enhancement images were applied and considered necessary to improve the quality of images [4], [11]. To enhance CT scan imaging efficiency and obtain an accurate diagnosis.…”

Section: Proposed Methodsmentioning

confidence: 99%

Medical Image Enhancement Techniques

2022

IJCCCE

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“…An attempt is being made to reduce the network's error sum by modifying the connection weight [19]. To begin, a small value is assigned to the network's connectio n value, and then a training sample is chosen to calculate the gradient of error in comparison to that sample [35]. An attempt is being made to reduce the network's error sum by altering the weight of the connection [36].…”

Section: Structure Of the Adopted Annmentioning

confidence: 99%

Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System

Salim

Adnan

Mutlag

2023

ETJ

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 A 450 nm Underwater Wireless OpticalCommunication System was implemented in this study.  Bit error rate was measured in the tap water channel for 2M bit/s,10M bit/s, and 20M bit/s.  The optimization for striking a balance between low power consumption and reliable data transfer in underwater ambient was investigated.  A FFBP-ANN model-based transmission power control for the UWOC system has been adopted.  power needed in multiple scenarios.Several scientists have proposed using an underwater wireless optical communication system (UWOC) to deliver high-speed data services using the abundant optical spectrum. However, wireless optical signal propagation faces an antagonistic environment when using undersea channels due to various factors like scattering, absorption, turbulence, and optical link misalignment between the transmitter and the receiver. These factors will attenuate the optical signal and lead to degrading system performance. To reduce these factors impact on the communication system's performance, transmitted optical power (OTP) should be increased. Since the UWOC system is battery -powered, increasing OTP will consume more electrical power. Therefore, it is necessary to adjust OTP to a value commensurate with the underwater channel changes. So, an ANN model is proposed in this article for link adaptation, which can adjust the OTP level in tandem with the underwater channel conditions. Data for training, testing, and validation of the proposed system reliability was collected experimentally, and tap water was used as a transmission medium. Evaluation of the proposed model outcome demonstrates that reliable performance is achieved in predicting OTP needed in multiple scenarios. The M SE of the predicted OTP is(9.5×10-3,1.5×10-2, and 1.7×10-2) dBm in the training, testing, and validation stages, respectively. The regression values of the training, testing and validation sets are (0.9997,0.9990, and 0.9996). The results achieved by the prop osed model prove it is reliable to be applied in UWOC systems.

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“…This has traditionally been done using the back propagation technique, which seeks to reduce the MSE. The backpropagation (BP) algorithm is used to display a multilayer perceptron (MLP) algorithm which can be trained with a BP algorithm [30]. On the other hand, backpropagation with a hidden layer between the input and output layer may generalize the ordinary perception and may also estimate any continuous function to a reasonable degree of accuracy with a small number of hidden layers, as stated [27].…”

Section: Multilayer Perceptron Algorithm (Mpa)mentioning

confidence: 99%

A Comparative Study of Regression Machine Learning Algorithms: Tradeoff Between Accuracy and Computational Complexity

Gzar¹,

Mahmood²,

Abbas³

2022

MMEP

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The computational complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers which is the determination of looking for the best methods to solve a problem. The accuracy of a regression model's predictions must be reported as an error. According to the researchers, the most problematic issue is the lack of a properly defined machine learning assessment. In this research, Various types of machine learning regression algorithms, namely, Linear Regression, Support Vector Regression, Random Forest Regression, and Multilayer Perceptron Neural Network have been used to process and analyze the collected data in terms of comparison of their accuracy and the computational complexity. The applied dataset was collected using IoT sensors seeking an appropriate algorithm that is the fittest to the collected data to design a model system that represents the goal of specific future applications. The result shows that the Random Forest regression has the highest computational complexity and highest accuracy depending on the calculated error metrics (Mean Square Error, Mean Absolute Error, and R Squared score) which are (0.0002, 0.005, and 0.995) respectively. Based on that, Random Forest Regression will be adapted and implemented with the structure of a planned design system.

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Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Cited by 12 publications

References 12 publications

Medical Image Enhancement Techniques

Medical Image Enhancement Techniques

Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System

A Comparative Study of Regression Machine Learning Algorithms: Tradeoff Between Accuracy and Computational Complexity

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