This paper aims to present a comprehensive review of advanced techniques and models
with a specific focus on deep neural network (DNN) for resource-constrained environments …

Purpose The prevalence of breast cancer continues to be a major public health concern.
Timely detection is vital for effective treatment and patient survival. In order to appropriately …

This paper presents an empirical study on advanced Deep Neural Network (DNN) models,
with a focus on identifying potential baseline models for efficient deployment in resource …

Breast cancer classification plays a crucial role in healthcare, especially in the diagnosis
and monitoring of patients. Traditional methods for classifying breast cancer based on …

Breast cancer (BC) diagnosis remains a significant challenge in the field of pathology,
requiring advanced tools for early and accurate detection [1]-[4]. The development of image …

Sažetak Multi-class classification of breast cancer pathological images remains challenging
due to complex image features and limited datasets. This study proposes SSResNeXt, a …

New and improved methods of diagnosis are needed because breast cancer is still the
leading cancer-related killer worldwide. Updates to the methods used to categorize breast …

Early detection is paramount in the fight against breast cancer, as it can significantly improve
a patient's survival chances. Studies suggest early detection can increase survival rates. The …

Analyzing Baseline Models for Optimizing Deep Neural Networks in Resource-Constrained
Environments Page 1 2024, Volume 5 Issue 2 81 *Corresponding Author: Raafi Careem (mraafi@gmail.com) …

Objective: To investigate the influence of different feature scaling techniques on the
performance of machine learning algorithms in breast cancer prediction and identify the …