Background: The cancer of colon is one of the important cause of morbidity and mortality in adults. For the management of colonic carcinoma, the definitive diagnosis depends on the histological examination of biopsy specimens. With the development of whole slide imaging, the convolutional neural networks are being applied to diagnose colonic carcinoma by digital image analysis. Aim: The main aim of the current study is to assess the application of deep learning for the histopathological diagnosis of colonic adenocarcinoma by analysing the digitized pathology images. Materials & Methods: The images of colonic adenocarcinoma and non neoplastic colonic tissue have been acquired from the two datasets. The first dataset contains ten thousand images which were used to train and validate the convolutional neural network (CNN) architecture. From the second dataset (Colorectal Adenocarcinoma Gland (CRAG) Dataset) 40% of the images were used as a train set while 60% of the images were used as test dataset. Two histopathologists also evaluated these images. In this study, three variants of CNN (ResNet-18, ResNet-34 and ResNet-50 ) have been employed to evaluate the images. Results: In the present study, three CNN architectures(ResNet-18, ResNet-30, and ResNet-50) were applied for the classification of digitized images of colonic tissue. The accuracy (93.91%) of ResNet-50 was the highest which is followed by ResNet-30 and ResNet-18 with the accuracy of 93.04% each. Conclusion: Based on the findings of the present study and analysis of previously reported series, the development of computer aided technology to evaluate the surgical specimens for the diagnosis of malignant tumors could provide a significant assistance to pathologists.

BackgroundPneumonia is a leading cause of morbidity and mortality worldwide, particularly among the developing nations. Pneumonia is the most common cause of death in children due to infectious etiology. Early and accurate Pneumonia diagnosis could play a vital role in reducing morbidity and mortality associated with this ailment. In this regard, the application of a new hybrid machine learning vision-based model may be a useful adjunct tool that can predict Pneumonia from chest X-ray (CXR) images.Aim & Objectivewe aimed to assess the diagnostic accuracy of hybrid machine learning vision-based model for the diagnosis of Pneumonia by evaluating chest X-ray (CXR) imagesMaterials & MethodsA total of five thousand eight hundred and fifty-six digital X-ray images of children from ages one to five were obtained from the Chest X-Ray Pneumonia dataset using the Kaggle site. The dataset contains fifteen hundred and eighty-three digital X-ray images categorized as normal, where four thousand two hundred and seventy-three digital X-ray images are categorized as Pneumonia by an expert clinician. In this research project, a new hybrid machine learning vision-based model has been evaluated that can predict Pneumonia from chest X-ray (CXR) images. The proposed model is a hybrid of convolutional neural network and tree base algorithms (random forest and light gradient boosting machine). In this study, a hybrid architecture with four variations and two variations of ResNet architecture are employed, and a comparison is made between them.ResultsIn the present study, the analysis of digital X-ray images by four variations of hybrid architecture RN-18 RF, RN-18 LGBM, RN-34 RF, and RN-34 LGBM, along with two variations of ResNet architecture, ResNet-18 and ResNet-30 have revealed the diagnostic accuracy of 97.78%, 96.42%, 97.1%,96.59%, 95.05%, and 95.05%, respectively.DiscussionThe analysis of the present study results revealed more than 95% diagnostic accuracy for the diagnosis of Pneumonia by evaluating chest x-ray images of children with the help of four variations of hybrid architectures and two variations of ResNet architectures. Our findings are in accordance with the other published study in which the author used the deep learning algorithm Chex-Net with 121 layers.ConclusionThe hybrid machine learning vision-based model is a useful tool for the assessment of chest x rays of children for the diagnosis of Pneumonia.