The enactment of automatic medial image taxonomy using customary methods of machine learning and data mining mostly depend upon option of significant descriptive characteristics obtained from the medical images. Reorganization of those skins obliges domain-specific skillful awareness moreover not a forthright process. Here in this paper we are going to propose a deep learning based cnn's named as deep cnn architecture. Which is a generic architecture and it accepts input as medical image data and produces the class or type of the decease. And we made comparison with the classical models like svm and elm. RÉSUMÉ. L'adoption d'une taxonomie automatique des images médianes à l'aide des méthodes habituelles d'apprentissage automatique et d'exploration de donné es dé pend principalement de l'option de caractéristiques descriptives significatives obtenues à partir des images mé dicales. La ré organisation de ces skins né cessite en outre une connaissance approfondie du domaine spé cifique, et non un processus direct. Dans cet article, nous allons proposer un CNN's basée sur l'apprentissage profond appelée architecture profonde de CNN qui est une architecture gé né rique. Elle accepte les entré es en tant que donné es d'images mé dicales et produit la classe ou le type de dé cè s. Et nous avons comparé les modè les classiques comme SVM et ELM.
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