Current Trends in Breast Cancer Pathology, Screening, Diagnosis and Treatments

The book "Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches" discusses how advanced deep learning techniques enhance medical image analysis. These advances offer promising progress in healthcare through improvements in diagnostic accuracy, efficiency in medical image interpretation, and breakthroughs in treatment planning.

The book begins by explaining foundational concepts of deep learning and Convolutional Neural Networks (CNNs) to show how they extract meaningful features from medical images for tasks such as diagnosis and segmentation. It then explores Kronecker convolutions, highlighting their ability to better capture spatial hierarchies, use parameters more efficiently, and adapt to unique medical image characteristics. Subsequent sections cover applications like tumor detection, organ segmentation, and disease classification and examine real-world implementations of AI in diagnostic imaging, precision medicine, and continuous health monitoring through wearable devices. The final section addresses challenges, emerging trends, and future directions, emphasising how these techniques could shape advanced healthcare. Throughout the book, the authors bridge medicine, computer science, and machine learning to address complex problems in medical imaging and healthcare.

. Investigates opportunities and challenges of deep learning, including convolutional neural networks (CNNs) and their applications in medical image processing

. Includes comprehensive examination and elucidation of Kronecker convolutional procedures and their significance in medical image processing

. Explores specific medical imaging tasks where Kronecker convolutions prove beneficial

. Provides detailed examples demonstrating how convolutions may be employed to improve healthcare, offering insights into how deep learning is currently being used in clinical settings