Medical Image Classification Using Multi-Vocabulary
نویسندگان
چکیده
منابع مشابه
Image Classification using Adaptive Multi-Module
For a classification using neural network, there exist many cases in which the distributions of classes are so complex that the classification with single network does not properly differentiate the given data into classes. This problem can be resolved if we employ multiple modules that can classify different data respectively. This paper proposes a new adaptive architecture for classification ...
متن کاملTissue Image Classification Using Multi-Fractal Spectra
Tissue image classification is a challenging problem due to the fact that the images contain highly irregular shapes in complex spatial arrangement. The multi-fractal formalism has been found useful in characterizing the intensity distribution present in such images, as it can effectively resolve local densities and also represent various structures present in the image. This paper presents a d...
متن کاملImage Multi-Classification using PHOW Features
Automatic labeling and classification of a vast number of images is a huge challenge, so machines are used as a part of image classification and annotation is turned into a prerequisite to adapt to the high improvement of advanced digital image innovations consistently. Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and recognition; this descriptor is u...
متن کاملRegularized Tensor Factorization for Multi-Modality Medical Image Classification
This paper presents a general discriminative dimensionality reduction framework for multi-modal image-based classification in medical imaging datasets. The major goal is to use all modalities simultaneously to transform very high dimensional image to a lower dimensional representation in a discriminative way. In addition to being discriminative, the proposed approach has the advantage of being ...
متن کاملWND-CHARM: Multi-purpose image classification using compound image transforms
We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Asian Journal of Applied Sciences
سال: 2014
ISSN: 1996-3343
DOI: 10.3923/ajaps.2015.71.78