Deep CNN frameworks comparison for malaria diagnosis - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Deep CNN frameworks comparison for malaria diagnosis


We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstained images.
Fichier principal
Vignette du fichier
Pattanaik, Wang, Horain - Deep CNN Based Framework for Malaria Diagnosis.pdf (891.98 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02280412 , version 1 (06-09-2019)



Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain. Deep CNN frameworks comparison for malaria diagnosis. IMVIP 2019 Irish Machine Vision and Image Processing Conference, Aug 2019, Dublin, Ireland. ⟨hal-02280412⟩
101 View
57 Download



Gmail Facebook X LinkedIn More