Classification of medical datasets using back propagation neural network powered by genetic-based features elector

Lafta, Hussein Attya; Hasan, Zainab Falah; Ayoob, Noor Kadhim

doi:10.11591/ijece.v9i2.pp1379-1384

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…The quantitative analysis is based on four statistical performance parameters which are accuracy, precision, recall, and F-score that are primarily used in image segmentation studies as described in research papers [11], [13], [18], [24], [25]. The accuracy test determines how well a diagnostic test identifies and rules out a specific condition.…”

Section: Performance Evaluationmentioning

confidence: 99%

A new procedure for lung region segmentation from computed tomography images

Abdullah

Sulaiman²,

Osman³

et al. 2022

IJECE

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Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.

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Section: Performance Evaluationmentioning

confidence: 99%

A new procedure for lung region segmentation from computed tomography images

Abdullah

Sulaiman²,

Osman³

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…In this stage, popular criteria are used to assess the output of machine learning models to measure the classification model's ability to classify the testing dataset accurately. Accuracy, precision, recall, and F1-score are the common measurement tools, as mentioned in ( 5) to (8), respectively. True positive, false positive, and false negative predictions for a given class label are represented by the variables TP, FP, and FN, respectively [30], [31].…”

Section: Evaluation Stagementioning

confidence: 99%

“…Presently, no treatment or drug can delay or stop the growth of Alzheimer's disease, necessitating and requiring effective and precise methods for early detection and preventing the disease from progressing to late stages. The practice of assigning items to a specific set based on their characteristics is called classification [8]. One of the specific goals of artificial intelligence research is to classify diseases [9].…”

Section: Introductionmentioning

confidence: 99%

A modified residual network for detection and classification of Alzheimer’s disease

Hanoon

Alasadi

2022

IJECE

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Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer ResNet is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.

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“…MRI is currently the most important way of obtaining soft tissue imaging especially in oncology, since the image contrasts and resolution of lesions and healthy tissue are significantly improved [15], [16]. The MRI is considered to be more accurate to assess the level of cancer infiltration than computed tomography [17]- [19]. The registration of biomedical images has many approaches, gold standard uses region-of-interest markers, and other methods include correlation of geometrical characteristics [20], [21].…”

Section: Introductionmentioning

confidence: 99%

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

Ahmed

Salah

2022

IJEECS

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The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.

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Classification of medical datasets using back propagation neural network powered by genetic-based features elector

Cited by 4 publications

References 6 publications

A new procedure for lung region segmentation from computed tomography images

A new procedure for lung region segmentation from computed tomography images

A modified residual network for detection and classification of Alzheimer’s disease

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

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