Enhanced and Effective Computerized Multi Layered Perceptron based Back Propagation Brain Tumor Detection with Gaussian Filtering

Ayyappa, Yalanati; Bekkanti, Ashok; Krishna, Azmira; Neelakanteswara, P.; Basha, Cmak Zeelan

doi:10.1109/icirca48905.2020.9182921

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…A hybrid intelligent system using the reduced incidence matrix was developed with four main processes, the computational cost decreased, but the accuracy was not high enough [6,7]. Multiple phases of segmentation were performed on the brain MRIs by feature extraction and classification using a radial basis function neural network and BPNN classifiers [9,10]. Brain MRIs were also classified in different pathological conditions by feedforward neural networks [11,12].…”

Section: Related Workmentioning

confidence: 99%

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Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Ghahramani

Shiri

2022

IET Image Processing

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Magnetic resonance imaging (MRI) is a high-quality medical image that is used to detect brain tumours in a complex and time-consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg-Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey-level co-occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification.

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Section: Related Workmentioning

confidence: 99%

“…Multiple phases of segmentation were performed on the brain MRIs by feature extraction and classification using a radial basis function neural network and BPNN classifiers [9, 10]. Brain MRIs were also classified in different pathological conditions by feedforward neural networks [11, 12].…”

Section: Related Workmentioning

confidence: 99%

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Ghahramani

Shiri

2022

IET Image Processing

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“…To familiar with the knowledge of Multi-Layer perceptron, (Ayyappa et al 2020) was used where-in a computerized Tumor recognition procedure was proposed which helped various doctors in recognizing cerebrum tumors. Here, a solidarity MLP based Gaussian Filtering alongside BP Neural Network was evaluated which delivered good precise outcomes while distinguishing the cerebrum tumor with an exactness pace of 93% when contrasted with different classification methods like SVM and PNN.…”

Section: Related Workmentioning

confidence: 99%

New York City taxi trip duration prediction using MLP and XGBoost

Poongodi

Malviya

Kumar

et al. 2021

Int J Syst Assur Eng Manag

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New York City taxi rides form the core of the traffic in the city of New York. The many rides taken every day by New Yorkers in the busy city can give us a great idea of traffic times, road blockages, and so on. Predicting the duration of a taxi trip is very important since a user would always like to know precisely how much time it would require of him to travel from one place to another. Given the rising popularity of app-based taxi usage through common vendors like Ola and Uber, competitive pricing has to be offered to ensure users choose them. Prediction of duration and price of trips can help users to plan their trips properly, thus keeping potential margins for traffic congestions. It can also help drivers to determine the correct route which in-turn will take lesser time as accordingly. Moreover, the transparency about pricing and trip duration will help to attract users at times when popular taxi app-based vendor services apply surge fares. Thus in this research study, we used real-time data which customers would provide at the start of a ride, or while booking a ride to predict the duration and fare. This data includes pickup and drop-off point coordinates, the distance of the trip, start time, number of passengers, and a rate code belonging to the different classes of cabs available such that the rate applied is based on a regular or airport basis. Hereafter, we applied XGBoost and Multi-Layer Perceptron models to find out which one of them provides better accuracy and relationships between real-time variables. At last, a comparison of the two mentioned algorithms facilitates us to decide that XGBoost is more fitter and efficient than Multi-Layer Perceptron for taxi trip duration-based predictions.

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“…Research using backpropagation has been carried out to detect tumor diseases through X-ray images of the brain [12]. Compared with those studies, the proposed model achieves an improved accuracy of 93% in detecting tumor disease.…”

mentioning

confidence: 99%

Color Based Feature Extraction and Backpropagation Neural Network in Tamarind Turmeric Herb Recognition

Fauziyah

Adhisuwignjo

Afandi

et al. 2022

KINETIK

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The aim of this paper is finding the optimum image pattern of the tamarind turmeric herb. So far, in the process of producing tamarind turmeric herb, it is not constant in terms of taste and color, which is influenced by maturity and the amount of turmeric. Image pattern recognition will use Backpropagation algorithm applied to typical Content-based image retrieval systems. The main purpose is to apprehend various parts of tamarind turmeric herb in the retrieving processing. The camera is applied to classify the tamarind turmeric herb product, process into 5x5 pixels, and take an average of the RGB value so the stable RGB values will be obtained in each category and used as input for Backpropagation algorithm. The most suitable and the fastest process from the Backpropagation algorithm will be searched and applied in a real-time machine. In this paper will be using two methods, first, train the algorithm using ten data by change neuron, layer, momentum, and learning rate, and the last is testing with ten data. The results obtained from the training and testing algorithm that the two hidden layers can recognize 100% inputs, with three input layers used for R, G, and B value, ten neurons in the first hidden layers and the second hidden layers, one output layer with a parameter used is Learning rate 0.5 and Momentum 0.6. The best image pattern standard for tamarind turmeric herb is dark yellow with RGB values of 255, 102, 32 up to 255, 128, 48.

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Enhanced and Effective Computerized Multi Layered Perceptron based Back Propagation Brain Tumor Detection with Gaussian Filtering

Cited by 13 publications

References 13 publications

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

New York City taxi trip duration prediction using MLP and XGBoost

Color Based Feature Extraction and Backpropagation Neural Network in Tamarind Turmeric Herb Recognition

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