A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Ali, Farman; Khan, Sadia; Abbas, Arbab Waseem; Shah, Babar; Hussain, Takasar; Song, Dongho; EI-Sappagh, Shaker; Singh, Jaiteg

doi:10.32604/cmc.2022.024103

Cited by 11 publications

(6 citation statements)

References 31 publications

(35 reference statements)

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“…With no doubt about their effectiveness, it is rare for the proposed networks to be dedicated to a specific project and for their parameters to be documented on the basis of a specific goal. Networks that follow this process go beyond the narrow boundaries of the domain for which they were designed and are applied to other domains [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities

et al. 2022

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Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers. Eleven biomedical image datasets of various modalities are utilised, including SPECT, CT, photographs, microscopy, and X-ray. Seven state-of-the-art CNNs are employed and tuned to perform image classification in tasks. The main conclusion of the research is that DL reveals potential biomarkers in several cases, especially when the models are trained from scratch in domains where low-level features such as shapes and edges are not enough to make decisions. Furthermore, in some cases, device acquisition variations slightly affect the performance of DL models.

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Section: Methodsmentioning

confidence: 99%

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities

et al. 2022

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“…However, the computation required for this method is time-consuming and inefficient. Ali et al [ 29 ] proposed an approach by employing two deep learning approaches named the GoogleNet and YOLO models to recognize the brain tumors from the MRI samples. They attained the highest accuracy of 97% with the first model.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

Kibriya

Amin²,

Kim

et al. 2023

Sensors

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One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient’s health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method’s discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.

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“…These techniques are increasing in popularity because they have the capacity to perform at the human level in a variety of medical disciplines, such as cancer detection, diabetic retinopathy detection, neural connectomics, molecular drug activity, genomics, speech recognition, image recognition, disease modeling, and risk prediction. It is critical to comprehend how DL can save lives (Harerimana et al, 2019 ; Witt et al, 2019 ; Ali et al, 2021 ; Meszaros et al, 2022 ). DL is more successful than other ML algorithms since there is no need to invest additional time and effort in feature building with a domain expert; instead, the system can learn the features from raw data, classify the images, and localize tumors/diseases.…”

Section: Healthcare Analyticsmentioning

confidence: 99%

Healthcare analytics—A literature review and proposed research agenda

Elragal,

Habibipour

2023

Front. Big Data

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This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field.

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A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Cited by 11 publications

References 31 publications

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities

A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features

Healthcare analytics—A literature review and proposed research agenda

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