Zobrazeno 1 - 10
of 2 088
pro vyhledávání: '"Resnet50"'
Publikováno v:
Journal of Information Systems and Informatics, Vol 6, Iss 3, Pp 2099-2109 (2024)
The Oryza sativa (rice) plant is an important staple food source, especially in the Asian region. Rice production is often disrupted by diseases such as Brown Spot, Leaf Scald, Rice Blast, Rice Tungro, and Sheath Blight, which can reduce yield and cr
Externí odkaz:
https://doaj.org/article/0b8bd24fe0e94513ae873c9c26c48b5e
Publikováno v:
BioMedInformatics, Vol 4, Iss 3, Pp 1745-1756 (2024)
Background: Intracranial neoplasm, often referred to as a brain tumor, is an abnormal growth or mass of tissues in the brain. The complexity of the brain and the associated diagnostic delays cause significant stress for patients. This study aims to e
Externí odkaz:
https://doaj.org/article/54f70c6a359145198117391c0244b20e
Autor:
Niha Kamal Basha, Christo Ananth, K. Muthukumaran, Gadug Sudhamsu, Vikas Mittal, Fikreselam Gared
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)
Abstract The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convoluti
Externí odkaz:
https://doaj.org/article/c562bad41cc144a0982c996f11f259f2
Autor:
Yogesh Kumaran S, J. Jospin Jeya, Mahesh T R, Surbhi Bhatia Khan, Saeed Alzahrani, Mohammed Alojail
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-19 (2024)
Abstract Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent
Externí odkaz:
https://doaj.org/article/c9c175d940e045979152f8be7e06c164
Publikováno v:
Journal of Applied Science and Engineering, Vol 27, Iss 8, Pp 2961-2969 (2024)
Image classification tasks often compress the neural network model to reduce the number of parameters, which leads to a decrease in classification accuracy. herefore, we propose a novel ResNet50-based attention mechanism for image classification. Res
Externí odkaz:
https://doaj.org/article/77a51823cec341c5b490368b27b86078
Autor:
M. Mahendran, R. Visalakshi
Publikováno v:
Biomedical and Biotechnology Research Journal, Vol 8, Iss 2, Pp 181-186 (2024)
Background: Parkinson’s disease (PD) is a degenerative condition of the central nervous system primarily affecting the substantia nigra in the brain, resulting in the loss of dopamine-producing neurons and subsequent motor function deterioration. E
Externí odkaz:
https://doaj.org/article/36afc868d37149e5b272798faf97b6ba
Publikováno v:
International Journal Bioautomation, Vol 28, Iss 2, Pp 85-96 (2024)
Cataract, an age-related eye disease, poses a significant ophthalmological public health challenge in both developed and developing nations. Tailoring treatment or surgery plans helps accurately categorise the cataract's developmental stage. Precise
Externí odkaz:
https://doaj.org/article/6d45f1674c744e3784a4a9fd0de00de5
Autor:
Haihong Bian, Zhiyuan Zhang
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
Addressing issues such as high hardware costs, low recognition accuracy, and the inability to achieve fine-grained equipment classification, a non-invasive load fine-grained recognition system based on FPGA was developed and tested on a Linux system
Externí odkaz:
https://doaj.org/article/ba5a2dc178704e9b92881694984c6689
Publikováno v:
Zeitschrift für Medizinische Physik, Vol 34, Iss 2, Pp 278-290 (2024)
Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal ce
Externí odkaz:
https://doaj.org/article/6e093f9bc8234eabb7d82073c602cb19
Autor:
Yulrio Brianorman, Dewi Utami
Publikováno v:
Jurnal Informatika, Vol 12, Iss 1, Pp 61-70 (2024)
The classification of images from the Indonesian Sign Language System (SIBI) using VGG16, ResNet50, Inception, Xception, and MobileNetV2 Convolutional Neural Network (CNN) architectures is evaluated in this paper. With Google Colab Pro, a 224 × 224-
Externí odkaz:
https://doaj.org/article/58e9cc43caee4034b08f5f238c963d56