Zobrazeno 1 - 10
of 1 641
pro vyhledávání: '"efficientnet"'
Autor:
Yuri Pamungkas, Dwinka Syafira Eljatin
Publikováno v:
Jurnal Sisfokom, Vol 13, Iss 3, Pp 360-368 (2024)
Nowadays, malaria has become an infectious disease with a high mortality rate. One way to detect malaria is through microscopic examination of blood preparations, which is done by experts and often takes a long time. With the development of deep lear
Externí odkaz:
https://doaj.org/article/7bf2a5ac21af475ab891d284df0c0269
Autor:
A. M. J. MD. Zubair Rahman, R. Mythili, K. Chokkanathan, T. R. Mahesh, K. Vanitha, Temesgen Engida Yimer
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual interpretation, which is subject
Externí odkaz:
https://doaj.org/article/a70de337c7f2453a9a588b0f84ea073a
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Polycystic Ovary Syndrome (PCOS) is a widespread endocrinological dysfunction impacting women of reproductive age, categorized by excess androgens and a variety of associated syndromes, consisting of acne, alopecia, and hirsutism. It involve
Externí odkaz:
https://doaj.org/article/301511eceb314f87986d6c2f893087f9
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract To address the problem of dense crowd face detection in complex environments, this paper proposes a face detection model named Deep and Compact Face Detection (DCFD), which adopts an improved lightweight EfficientNetV2 network to replace the
Externí odkaz:
https://doaj.org/article/db16084cf05e4d708789ee175af8cf75
Autor:
M. Latha, P. Santhosh Kumar, R. Roopa Chandrika, T. R. Mahesh, V. Vinoth Kumar, Suresh Guluwadi
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-18 (2024)
Abstract Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and Dens
Externí odkaz:
https://doaj.org/article/1962d31a075248aa9f34cd4241ec0e96
Publikováno v:
Teknika, Vol 13, Iss 2, Pp 293-300 (2024)
COVID-19 dan penyakit paru-paru telah menjadi faktor utama penyebab kematian manusia di seluruh dunia. Kematian pasien dipengaruhi oleh keterlambatan deteksi dini. Sebagian besar profesional medis menggunakan gambar untuk mengidentifikasi kondisi par
Externí odkaz:
https://doaj.org/article/507b3cfeb7fe490dbfcdcc6931893b3d
Publikováno v:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol 12, Iss 1 (2024)
Age-related macular diseases (AMD) are common reason for visual impairment in humans. These anomalies can result from a variety of illnesses and disorders. Currently, skilled medical professionals make this diagnosis by visually inspecting the pictur
Externí odkaz:
https://doaj.org/article/5190f79064b3481cb09d0e2526f6f6a6
Alzheimer’s disease multiclass detection through deep learning models and post-processing heuristics
Publikováno v:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol 12, Iss 1 (2024)
Alzheimer’s disease (AD) significantly impacts millions globally, causing progressive memory loss and cognitive decline. While a cure remains elusive, early detection can mitigate effects and improve quality of life. Recent AD research has shown pr
Externí odkaz:
https://doaj.org/article/8b8dcd36e5d142188daa07eddb0e0ffb
Publikováno v:
Systems and Soft Computing, Vol 6, Iss , Pp 200093- (2024)
Automatically recognizing sheep breeds is highly valuable for the sheep farming industry, allowing farmers to pinpoint their specific business needs. Accurately distinguishing between sheep breeds poses a challenge for numerous farmers with limited e
Externí odkaz:
https://doaj.org/article/665cc9a153b8427bbb32fa16ba2a32f7
Publikováno v:
Ophthalmology Science, Vol 4, Iss 6, Pp 100555- (2024)
Objective: The aim of our research is to enhance the calibration of machine learning models for glaucoma classification through a specialized loss function named Confidence-Calibrated Label Smoothing (CC-LS) loss. This approach is specifically design
Externí odkaz:
https://doaj.org/article/b6d0d7fe770f46b489bb3d2514dbfc0e