Brain tumor classification using the fused features extracted from expanded tumor region

Öksüz, Coşku; Urhan, Oğuzhan; Güllü, M. Kemal

doi:10.1016/j.bspc.2021.103356

Cited by 47 publications

(23 citation statements)

References 45 publications

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“…Transfer learning is the transfer of a model that has been trained on a large dataset to solve a similar problem, together with its weights. Therefore, this approach eliminates the need to train the deep model from scratch and the large dataset required for training [54][55][56].…”

Section: Transfer Learningmentioning

confidence: 99%

A Novel Deep Transfer Learning-Based Approach for Automated Pes Planus Diagnosis Using X-ray Image

et al. 2023

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Pes planus, colloquially known as flatfoot, is a deformity defined as the collapse, flattening or loss of the medial longitudinal arch of the foot. The first standard radiographic examination for diagnosing pes planus involves lateral and dorsoplantar weight-bearing radiographs. Recently, many artificial intelligence-based computer-aided diagnosis (CAD) systems and models have been developed for the detection of various diseases from radiological images. However, to the best of our knowledge, no model and system has been proposed in the literature for automated pes planus diagnosis using X-ray images. This study presents a novel deep learning-based model for automated pes planus diagnosis using X-ray images, a first in the literature. To perform this study, a new pes planus dataset consisting of weight-bearing X-ray images was collected and labeled by specialist radiologists. In the preprocessing stage, the number of X-ray images was augmented and then divided into 4 and 16 patches, respectively in a pyramidal fashion. Thus, a total of 21 images are obtained for each image, including 20 patches and one original image. These 21 images were then fed to the pre-trained MobileNetV2 and 21,000 features were extracted from the Logits layer. Among the extracted deep features, the most important 1312 features were selected using the proposed iterative ReliefF algorithm, and then classified with support vector machine (SVM). The proposed deep learning-based framework achieved 95.14% accuracy using 10-fold cross validation. The results demonstrate that our transfer learning-based model can be used as an auxiliary tool for diagnosing pes planus in clinical practice.

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Section: Transfer Learningmentioning

confidence: 99%

A Novel Deep Transfer Learning-Based Approach for Automated Pes Planus Diagnosis Using X-ray Image

et al. 2023

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“…To extract the set of significant features from the input image, the convolutional neural network (CNN) model is employed [60]. In the CNN concept, the block is the basis for construction, with several blocks working together to build a single cellular structure.…”

Section: B Feature Extractionmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middleincome countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

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“…In [ 27 ], the authors integrated different classifiers, i.e., extra trees, random forest, and logistic regression, and achieved better prediction performance by utilizing the combined results obtained from the aforementioned classifiers. The authors in [ 7 ] presented an intelligent technique for classification. Another model proposed in [ 4 ] used fuzzy brain-storm optimization, which classifies brain tumors into gliomas, meningiomas, and pituitary categories.…”

Section: Related Workmentioning

confidence: 99%

“…However, the effectiveness and side effects depend on several factors, such as tumor grade, size, location, patient’s age, and overall health. Brain tumors are one of the biggest contributors to deaths in human beings worldwide [ 7 , 8 , 9 ]. Once a brain tumor is diagnosed, a patient’s survival rate increases by 36% for the next five years, thereby demanding the urgent attention of the research community [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

et al. 2023

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Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction challenging. Existing literature provides several survival prediction models that use different parameters, such as patient age, gross total resection status, tumor size, or tumor grade. However, accuracy is often lacking in these models. The use of tumor volume instead of size may improve the accuracy of survival prediction. In response to this need, we propose a novel model, the enhanced brain tumor identification and survival time prediction (ETISTP), which computes tumor volume, classifies it into low- or high-grade glioma, and predicts survival time with greater accuracy. The ETISTP model integrates four parameters: patient age, survival days, gross total resection (GTR) status, and tumor volume. Notably, ETISTP is the first model to employ tumor volume for prediction. Furthermore, our model minimizes the computation time by allowing for parallel execution of tumor volume computation and classification. The simulation results demonstrate that ETISTP outperforms prominent survival prediction models.

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Brain tumor classification using the fused features extracted from expanded tumor region

Cited by 47 publications

References 45 publications

A Novel Deep Transfer Learning-Based Approach for Automated Pes Planus Diagnosis Using X-ray Image

A Novel Deep Transfer Learning-Based Approach for Automated Pes Planus Diagnosis Using X-ray Image

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

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