Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit

Fahmi, Fahmi; Apriyulida, Fitri; Nasution, Sawaluddin; Sawaluddin,

doi:10.1155/2020/2483285

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…NS is an enhanced mathematical model that is applied to solve the problem of uncertain and ambiguous data. It includes a simple neutrosophic rule-based method in the form of; IF A AND B THEN Y to solve the problem rather than trying to design a system that is mathematically similar to the fuzzy classifier [48][49][50]. Thus, NT is similar to fuzzy technique.…”

Section: Alow-bestmentioning

confidence: 99%

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Shaban¹

2021

Preprint

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Breast cancer is one of the most common types of cancer that affects women globally and it is the primary cause of death. Early detection of breast cancer is a vital process that can facilitate appropriate treatment, stop the progression of cancer cells, and reduce morbidity and mortality. Artificial Intelligence (AI) and Machine Learning (ML) are the most popular methods that can be used to detect and classify breast cancer accurately. In this paper, a new strategy for classifying breast cancer using Neutrosophic Techniques (NTs) and machine learning techniques is introduced, which is called Breast Cancer Classification Strategy (BC2S). The proposed BC2S consists of two phases, which are; Data Preprocessing Phase (DP2) and Breast Cancer Classification Phase (BC2P). The main aim of the data preprocessing phase is to; (i) extract features from mammogram images and then remove the outlier items, (ii) select the most effective and informative features from those extracted features using new feature selection method called Efficient Ant Colony Optimization (EACO), and (iii) convert the selected features from classical domain into neutrosophic domain using NTs to give accurate classification through the next classification phase BC2P. The proposed classification model uses Deep Neural Network (DNN) to determine whether the patient is normal or infected with benign or malignant cancer. According to experimental results, the proposed strategy outperforms other competitors in terms of accuracy, precision, recall, and F-measure.

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Section: Alow-bestmentioning

confidence: 99%

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Shaban¹

2021

Preprint

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“…Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are methods for identifying normal and abnormal growing cells in the brain (Houssein et al, 2021b;Alsaif et al, 2022). The Computed Tomography (CT) scan is used to diagnose the patient using an X-ray and the computer to create the brain images in the axial fragments (Bekhet et al, 2020;Fahmi et al, 2020;Mubashar et al, 2022).…”

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confidence: 99%

Ensemble deep learning for brain tumor detection

Alsubai

Khan

Alqahtani

et al. 2022

Front. Comput. Neurosci.

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With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.

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Discrimination Between Stroke and Brain Tumour in CT Images Based on the Texture Analysis

Kobus

Sobczak

Jangas

et al. 2022

Advances in Intelligent Systems and Computing

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Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit

Cited by 4 publications

References 13 publications

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Ensemble deep learning for brain tumor detection

Discrimination Between Stroke and Brain Tumour in CT Images Based on the Texture Analysis

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