Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique

Singh, Vinayak; Gourisaria, Mahendra Kumar; Harshvardhan, GM; Rautaray, Siddharth Swarup; Pandey, Manjusha; Sahni, Manoj; León‐Castro, Ernesto; Espinoza-Audelo, Luis F.

doi:10.3390/app12062900

Cited by 18 publications

(4 citation statements)

References 47 publications

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“…Relevance [60] and saliency [61] were introduced, which can be defined as, A major part of the model is to evaluate it while observing the exactness and performance of classifiers on the test data and comparing the best from them. The confusion matrix [62] contains four outcomes produced by binary classifiers which can be used for describing the performance of the models. Various metrics such as recall accuracy [63], precision, AUC score, specificity, F1-score [64], and BAC were examined to verify and validate the results.…”

Section: Resultsmentioning

confidence: 99%

Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies

et al. 2022

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The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%.

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Section: Resultsmentioning

confidence: 99%

Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies

et al. 2022

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“…In the last few years, picture recognition has benefited greatly from data-based AI training techniques. Since the advent of AlexNet [3], Convolutional Neural Networks (CNNs) [4][5][6] have been the standard architecture for Computer Vision (CV) for a long time. This is followed by CNN architecture models such as VGGNet [7], Resnet [8], and EfficientNet [9].…”

Section: Introductionmentioning

confidence: 99%

Microbial Colony Detection Based on Deep Learning

Yang,

Zhong,

Yang

et al. 2023

Applied Sciences

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In clinical drug sensitivity experiments, it is necessary to plate culture pathogenic bacteria and pick suitable colonies for bacterial solution preparation, which is a process that is currently carried out completely by hand. Moreover, the problems of plate contamination, a long culture period, and large image annotation in colony plate image acquisition can lead to a small amount of usable data. To address the issues mentioned above, we adopt a deep learning approach and conduct experiments on the AGAR dataset. We propose to use style transfer to extend the trainable dataset and successfully obtain 4k microbial colony images using this method. In addition, we introduce the Swin Transformer as a feature extraction network in the Cascade Mask R-CNN model architecture to better extract the feature information of the images. After our experimental comparison, the model achieves a mean Average Precision (mAP) of 61.4% at the Intersection over Union (IoU) [0.50:0.95]. This performance surpasses that of the Cascade R-CNN with HRNet, which is the top-performing model in experiments conducted on the AGAR dataset, by a margin of 2.2%. Furthermore, we perform experiments using YOLOv8x on the AGAR dataset, which results in a mAP of 76.7%.

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“…Therefore, early cancer identification improves the patient’s survival chances. According to a National Brain Tumor Foundation (NBTF) report [ 4 ], around 29,000 persons in the USA have primary malignant tumors, and 13,000 people die due to this type of brain tumor.…”

Section: Introductionmentioning

confidence: 99%

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

Almalki

Ali

Ahmed³

et al. 2022

Life

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Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.

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Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique

Cited by 18 publications

References 47 publications

Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies

Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies

Microbial Colony Detection Based on Deep Learning

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

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