Deep CNN frameworks comparison for malaria diagnosis

Priyadarshini Adyasha Pattanaik (1, 2) , Zelong Wang (3) , Patrick Horain (1, 2, 4)

1 INTERMEDIA - Interactions pour le multimedia
2 TSP - EPH - Département Electronique et Physique
3 TSP - Télécom SudParis
4 IP Paris - Institut Polytechnique de Paris

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.

Mots clés: DCNN Framework; Deep Learning; AlexNet; Malaria; VGGNet

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Imagerie médicale; Intelligence artificielle [cs.AI]

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⟩