Prof. Dr. Bilge Ayca Kirmizi, akarabork@yahoo.com 1 Introduction Breast cancer is the most frequently diagnosed form of cancer and is the second leading cause of death caused by cancer in women. In order to diagnose breast cancer type, stage, and grade accurately, examination of tissue biopsies and operation specimens is necessary. The biopsy specimens must be fixed embedded in paraffin blocks, mounted on glass slides and stained. Hematoxylin and Eosin (H&E), is a routine stain used in pathology laboratories all over the globe, which gives a good contrast of a tissue section and is commonly used to identify nuclei and cytoplasm [1]. Nevertheless, histopathological examination of the prapared slides involves laborious, time - consuming processes that are limited by specimen quality and pathologist experience.

Prof. Dr. Bilge Ayca Kirmizi, akarabork@yahoo.com 1 Introduction Nottingham Grading System [1] emphasizes three key morphological features on Hematoxylin and Eosin (H&E) stained slides to grade breast cancer: mitotic count, tubule formation, and nuclear pleomorphism. Mitotic count is the most prominent feature among them. Searching for mitosis on glass slides is a routine procedure for breast pathologists. Since there are so many high power fields (HPFs) on a single slide and mitotic cells vary in appearance, it is a tedious and time - consuming task. Additionally, mitotic cell judgment is somewhat subjective, making it difficult for pathologists to reach a consensus. Thus, it is extremely important to develop automatic detection methods that will not only save time and material resources, but will also enhance the reliability of pathological diagnosis.

The ongoing COVID-19 pandemic continues to pose significant challenges to global public health, despite the widespread availability of vaccines. Early detection of the disease remains paramount in curbing its transmission and mitigating its impact on public health systems. In response, this study delves into the application of advanced machine learning (ML) techniques for predicting COVID-19 infection probability. We conducted a rigorous investigation into the efficacy of various ML models, including XGBoost, LGBM, AdaBoost, Logistic Regression, Decision Tree, RandomForest, CatBoost, KNN, and Deep Neural Networks (DNN). Leveraging a dataset comprising 4000 samples, with 3200 allocated for training and 800 for testing, our experiment offers comprehensive insights into the performance of these models in COVID-19 prediction. Our findings reveal that Deep Neural Networks (DNN) emerge as the top-performing model, exhibiting superior accuracy and recall metrics. With an impressive accuracy rate of 89%, DNN demonstrates remarkable potential in early COVID-19 detection. This underscores the efficacy of deep learning approaches in leveraging complex data patterns to identify COVID-19 infections accurately. This study underscores the critical role of machine learning, particularly deep learning methodologies, in augmenting early detection efforts amidst the ongoing pandemic. The success of DNN in accurately predicting COVID-19 infection probability highlights the importance of continued research and development in leveraging advanced technologies to combat infectious diseases. I. INTRODUCTION Health represents the cornerstone of any society, yet the world currently grapples with a profound health crisis due to the widespread dissemination of the coronavirus. The global COVID-19 pandemic has inflicted extensive loss of life and has profoundly impacted individuals, both directly and indirectly.

Time Series Classification (TSC) is essential in many fields, such as medicine, environmental science and finance, enabling tasks like disease diagnosis, anomaly detection, and stock price analysis. Machine learning models for TSC like Recurrent Neural Networks and InceptionTime, while successful in numerous applications, can face scalability limitations due to intensive computational requirements. To address this, efficient models such as ROCKET and its derivatives have emerged, simplifying training and achieving state-of-the-art performance by utilizing a large number of randomly generated features from time series data. However, due to their random nature, most of the generated features are redundant or non-informative, adding unnecessary computational load and compromising generalization. Here, we introduce Sequential Feature Detachment (SFD) as a method to identify and prune these non-essential features. SFD uses model coefficients to estimate feature importance and, unlike previous algorithms, can handle large feature sets without the need for complex hyperparameter tuning. Testing on the UCR archive demonstrates that SFD can produce models with $10\%$ of the original features while improving the accuracy $0.2\%$ on the test set. We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy, called Detach-ROCKET. When applied to the largest binary UCR dataset, Detach-ROCKET is able to improve test accuracy by $0.6\%$ while reducing the number of features by $98.9\%$. Thus, our proposed procedure is not only lightweight to train and effective in reducing model size and enhancing generalization, but its significant reduction in feature count also paves the way for feature interpretation.

Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. Against this backdrop, this study examines a new and original dataset using the deep learning algorithm, and visualizes the output with gradient-weighted class activation mapping (Grad-CAM), one of the XAI applications. Afterwards, a detailed questionnaire survey was conducted with the pathologists on these images. Both the decision-making processes and the explanations were verified, and the accuracy of the output was tested. The research results greatly help pathologists in the diagnosis of paratuberculosis.