The early detection of cancer is vital to improving patient outcomes and survival rates. Recent advancements in the Internet of Things (IoT) and Internet of Medical Things (IoMT) technologies are aiding in more accurate, accessible, and efficient cancer screening methods. This week, we explore research into two different technologies for the early detection of cancer, one for breast cancer and the other for lung cancer.
Cloud Computing Solution for Early Detection of Breast Cancer
The Intelligent e-healthcare model for breast cancer (IEMBC) is an IoMT application that aims to help in the early detection of breast cancer and provide a low-cost, high-accuracy solution for breast cancer screening, particularly in remote areas with limited access to healthcare resources.
Key Features of IEMBC
- Hybrid cross-entropy thresholding technique for accurate extraction of infected regions
- Parallel boosting approach for improved performance in cloud-based e-healthcare services
- Deep learning approach for higher accuracy in breast cancer identification
Performance Highlights
- 93% segmentation accuracy achieved with the IEMBC-HGG unit
- 66.87% reduction in processing time using the parallel boosting approach (IEMBC-PB)
- 86.32% accuracy in breast cancer classification with the IEMBC-DC unit
The IEMBC model demonstrates the potential of IoMT technology in advancing breast cancer screening, and as a solution for early detection of cancer in underserved areas. Early detection of lung cancer allows physicians to begin prompt treatment which can help improve cancer patient outcomes. An approach utilizing Nose-on-Chip nanobiosensors for analyzing breath biomarkers is being tested in this field.
Early Detection of Lung Cancer Breath Biomarkers
Early detection of lung cancer allows physicians to begin prompt treatment which can help improve cancer patient outcomes. An approach utilizing Nose-on-Chip nanobiosensors for analyzing breath biomarkers is being tested in this field.
Key Aspects of Nose-on-Chip Nanobiosensors
- High sensitivity and selectivity in detecting lung cancer biomarkers in breath samples
- Potential for real-time, non-invasive lung cancer screening
- Integration of nanomaterials for enhanced sensing capabilities
The integration of modern technologies such as machine learning, IoT connectivity, and health informatics can be used to enhance lung cancer diagnostics. The development of IoT and IoMT technologies for early detection of cancer, particularly in breast and lung cancer, represents a significant step forward in improving cancer screening and patient outcomes.
