Results 151 to 160 of about 28,970 (210)

Deep Learning Artificial Intelligence and Restriction Spectrum Imaging for Patient-level Detection of Clinically Significant Prostate Cancer on Biparametric Magnetic Resonance Imaging. [PDF]

Eur Urol Open Sci
Song Y, Domingo MR, Conlin CC, Do DD, Baxter MT, Dornisch A, Xu G, Bagrodia A, Barrett T, Harisinghani M, Hollenberg G, Kamran S, Kane CJ, Kessler DA, Kuperman J, Lee K, Liss MA, Margolis DJA, Murphy PM, Nakrour N, Ngyuen T, Osinski TL, Rakow-Penner R, Roychowdhury S, Shabik AS, Trecarten S, Wehrli N, Weinberg EP, Woolen SA, Dale AM, Seibert TM.   +30 more
europepmc   +1 more source

An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars. [PDF]

Sci Rep
Buyukcakir B, De Tobel J, Thevissen P, Vandermeulen D, Claes P.   +4 more
europepmc   +1 more source

PneuNet: a lightweight convolutional neural network with multiscale feature fusion for automated pneumonia detection from chest X-rays. [PDF]

Front Med (Lausanne)
Saranyaraj D, Shrinaath V, Nayak A, Vishal R.   +3 more
europepmc   +1 more source

An ensemble of vision and swin transformers with LLM-based explanations for sugarcane leaf disease diagnosis. [PDF]

Sci Rep
Saritha M, Rasane K.
europepmc   +1 more source

A Multi-Stage Hybrid Learning Model with Advanced Feature Fusion for Enhanced Prostate Cancer Classification. [PDF]

Diagnostics (Basel)
Abd El-Ghany S, Abd El-Aziz AA.
europepmc   +1 more source

MangoLeafNet-XAI: an attention-enhanced deep learning architecture for accurate and interpretable mango leaf disease classification. [PDF]

Front Plant Sci
Rahman MA, Bhuiyan MTA, Noori FM, Uddin MZ, Masum AKM.   +4 more
europepmc   +1 more source

Hybrid Deep Learning for Brain Tumor Detection: Combining DenseNet and Custom CNN for Enhanced Accuracy. [PDF]

Biomed Eng Comput Biol
Swaminathan AD, Begum A, Ramamoorthy K, Nadarajan RN, Krishnamoorthy S, Balachandran PK, Kannan S.   +6 more
europepmc   +1 more source

End-to-end vs. human-defined feature extraction: comparing deep learning approaches for age classification using mandibular third molars. [PDF]

J Forensic Odontostomatol
Upalananda W, Charuakkra A, Chaichulee S.   +2 more
europepmc   +1 more source