Publications by authors named "Victor K Agbesi"
Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers.
View Article and Find Full Text PDFFeature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both semantic and contextual information from text at various levels of granularity while avoiding overfitting. Prior methods have often demonstrated suboptimal performance, largely due to the limitations of the feature extraction techniques employed.
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- This study aims to develop an intelligent predictive system for early detection and accurate prediction of cardiovascular disease (CVD), utilizing deep learning and data mining techniques.
- The system involves key steps like data preprocessing, feature selection, and disease classification, enhancing overall prediction effectiveness.
- Four machine learning models achieved high accuracies, with XG-Boost reaching 99.00%, while the proposed CardioVitalNet achieved 87.45% accuracy, providing insights for better medical diagnostics.
According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and effective deep learning frameworks to tackle this disease. However, poor feature extraction from the Chest X-ray images and the high computational cost of the available models impose difficulties to an accurate and fast Covid-19 detection framework.
View Article and Find Full Text PDFRecent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)-augmentation and random augmentation (RA) approaches to dealing with imbalanced data.
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- Recent advancements in machine learning, particularly deep learning, help in recognizing and classifying COVID-19 in medical images, but they struggle with feature extraction, which leads to less accurate results.
- This study introduces Dual_Pachi, an innovative framework designed for improved feature extraction from chest X-rays, utilizing a structure that includes converting images into specific color spaces and employing a multi-head self-attention mechanism.
- Testing shows that Dual_Pachi significantly outperforms traditional deep learning methods, achieving high accuracy levels while also providing visual insights into how attention is applied in the classification process.