with the growth of technology, supervising systems are increasingly replacing humans in military, transportation, medical, spatial, and other industries. among these systems are machine vision systems which are based on image processing and analysis. one of the important tasks of image processing is classification of images into desirable categories for the identification of objects or their sp...

We describe the medical image classification task in ImageCLEF 2005– 2009. It evolved from a classification task with 57 classes on a total of 10,000 images into a hierarchical classification task with a very large number of potential classes. Here, we describe how the database and the objectives changed over the years and how state–of–the–art approaches from machine learning and computer visio...

With the growth of technology, supervising systems are increasingly replacing humans in military, transportation, medical, spatial, and other industries. Among these systems are machine vision systems which are based on image processing and analysis. One of the important tasks of image processing is classification of images into desirable categories for the identification of objects or their sp...

background: we aimed to extract the histogram features for text analysis and, to classify the types of bio medical waste (bmw) for garbage disposal and management. methods: the given bmw was preprocessed by using the median filtering technique that efficiently reduced the noise in the image. after that, the histogram features of the filtered image were extracted with the help of proposed modifi...

Medical Image segmentation is to partition the image into a set of regions that are visually obvious and consistent with respect to some properties such as gray level, texture or color. Brain tumor classification is an imperative and difficult task in cancer radiotherapy. The objective of this research is to examine the use of pattern classification methods for distinguishing different types of...

Highly imbalanced datasets are ubiquitous in medical image classification problems. In such problems, it is often the case that rare classes associated to less prevalent diseases severely under-represented labeled databases, typically resulting poor performance of machine learning algorithms due overfitting process. this paper, we propose a novel mechanism for sampling training data based on po...

Machine learning algorithms for medical diagnostics often require resource-intensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical use in the field. We investigate of model quantization and GPU-acceleration chest X-ray classification on edge devices. employ 3 types (dynamic range, float-16, full int8) which we tested trained Chest-XRay14 ...

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages extracting local features of images. However, due to locality convolution operation, it cannot deal with long-range relationships well. Recently, transformers been app...