Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy

It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible uretero-renoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the shpley additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots

Transfer Learning in Keratoconus Classification

Early detection of keratoconus will provide more treatment choices, avoid heavy treatments, and help stop the rapid progression of the disease. Unlike traditional methods of keratoconus classification, this study presents a machine learning-based keratoconus classification approach, using transfer learning, applied on corneal topographic images. Classification is performed considering the three corneal classes already cited : normal, suspicious and keratoconus. Keratoconus classification is carried out using six pretrained convolutional neural networks (CNN) VGG16, InceptionV3, MobileNet, DenseNet201, Xception and EfficientNetB0. Each of these different classifiers is trained individually on five different datasets, generated from an original dataset of 2924 corneal topographic images. Original corneal topographic images have been subjected to a special preprocessing before their use by different models in the learning phase. Images of corneal maps are separated in five different datasets while removing noise and textual annotation from images. Most of models used in the classification allow good discrimination between normal cornea, suspicious and keratoconus one. Obtained results reached classification accuracy of 99.31% and 98.51% by DenseNet201 and VGG16 respectively. Obtained results indicate that transfer learning technique could well improve performance of keratoconus classification systems

DLDiagnosis: A mobile and web application for diseases classification using Deep Learning

The detection and classification of several diseases is often carried out manually by specialists in several disciplines. Consequently, the diagnosis and the follow-up of the evolution of the diseases become more delicate and slower. The objective of this paper is to propose a system, in a web and mobile modes, allowing to detect and classify several diseases, such as brain cancer and diabetic retinopathy, according to different classes by a rigorous analysis and processing of images. Proposed software classify only image-based diseases and can assist, and not replace, specialists to propose the most appropriate therapeutic strategy to the patients according to their case, it makes it possible to follow patients over time by closely following the evolution of their diseases over diagnoses