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In the past fifteen years, the grow of using Bag of Words (BoW) method in the field of computer vision is visibly observed. In addition,-for the text classification and texture recognition, it can also be used in classification of images, videos, robot localization, etc. It is one of the most common methods for the categorization of text and objects. In text classification, the BoW method records the number of occurrences of each bag that is created for each instance type or word disregarding the order of the words or the grammar. And in visual scene classification it is based on clusters of local descriptors which are taken from the images disregarding the order of the clusters. The key idea is to generate a histogram for the words in the documents or the features in the images to represent the specified document or image. The BoW method is computationally and even conceptually is simpler than many other classification methods. For that reason, BoW based systems could record new and higher performance scores on common used benchmarks of text and image classification algorithms. This paper presents an overview of BoW, importance of BoW, how does it work, applications and challenges of using BoW. This study is useful in terms of introducing the BoW method to the new researchers and providing a good background with associated related works to the researchers that are working on the model.

This research introduces an approach to diagnose diseases from medical check-up test reports. The proposed approach is produced from Optical Character Recognition (OCR) technology to convert the hard copy test reports into editable textual data, Bag of Words (BoW) model as feature selection algorithm, Naive Bayes as classification algorithm, and AdaBoost technique to enhance the performance of the Naive Bayes classifier. The performance of the proposed approach is very good in terms of validity and can be used in diagnosing of diseases from medical check-up test reports. The proposed approach is trained on dedicated trained partitions of multiple medical datasets, and then tested on the testing sets partitioned from the original datasets. The proposed algorithm is compared with the Support Vector Machine (SVM), Naive Bayes (NB), Decision Table (DT), and k-Nearest Neighbors (k-NN) classifiers, in which all the algorithms are tested on the same datasets. The proposed algorithm showed higher accuracy than the other four classifiers. So, the proposed approach which is the combination of BoW with AdaBoost technique is used to predict the name of the diseases from the medical check-up test reports. After that, an image as an example of the disease will be presented as well with the name of the disease to the physician and the patient. The image presentation is very important for the patients, because they may not familiar with the medical terms and disease names. Finally, the proposed approach can be used in the medical area because of its good performance and showing validated results after it is tested.