Advancements in machine visions for fruit sorting and grading: A bibliometric analysis, systematic review, and future research directions

Olorunfemi, Benjamin Oluwamuyiwa; Nwulu, Nnamdi I.; Adebo, Oluwafemi Ayodeji; Kavadias, Kosmas A.

doi:10.1016/j.jafr.2024.101154

Cited by 3 publications

(1 citation statement)

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“…At this stage, the image file size becomes smaller, thereby speeding up the computing process. The purpose of this resizing is to ensure consistency and uniformity in the data to be used, support computational efficiency, and meet the model requirements [16].…”

Section: Resizementioning

confidence: 99%

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

Rhomadhon,

Ningtias

2024

J. Soft Comput. Explor.

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According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.

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Section: Resizementioning

confidence: 99%