Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model.

The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures.

Skin Diseases Classification Using Hybrid AI Based Localization Approach.

One of the most prevalent diseases that can be initially identified by visual inspection and further identified with the use of dermoscopic examination and other testing is skin cancer. Since eye observation provides the earliest opportunity for artificial intelligence to intercept various skin images, some skin lesion classification algorithms based on deep learning and annotated skin photos display improved outcomes. The researcher used a variety of strategies and methods to identify and stop diseases earlier.

CNN-Based Cross-Modal Residual Network for Image Synthesis.

This study attempts to address the issue that present cross-modal image synthesis algorithms do not capture the spatial and structural information of human tissues effectively. As a consequence, the resulting photos include flaws including fuzzy edges and a poor signal-to-noise ratio. The authors offer a cross-sectional technique that combines residual modules with generative adversarial networks.

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task.

An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective.

Development and validation of a structured feedback questionnaire from postgraduates on various elements of postgraduate medical curriculum.

Background: Medical Council of India, introduced the Post Graduate (PG) curriculum as 'Competency Based Medical Education' (CBME). Feedback from the end users is a vital step in curriculum evaluation. Therefore, the primary objective of this study was to develop and validate a Structured Feedback Questionnaire (SFQ) for postgraduates, encompassing all the components of the PG-CBME curriculum.