Background: Having been known as a virulent disease in 1970s, cancer is now onsidered a chronic disease and 64% of cancer patients live for five years after diagnosis. Home care has gradually gained more importance and it is a great burden on the shoulders of caregivers. Caregivers have to undertake the responsibility of the cancer patient's home management, and organize care and arrange health care services according to the everchanging condition of patients. Caregivers should be prepared for home care so they can provide accurate and complete care to patients. This descriptive study aims to investigate challenges that caregivers encounter in the home care of patients and the reasons for these challenges. Conclusions: This study, it was found that caregivers experience challenges due to following factors: patient nutrition, medicine use, oral and body hygiene, colostomy maintenance and stomach tube feeding, concern of dropping the patient, feeling incompetency in body temperature and fever control, fatigue, and lack of personal time.
Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches, the study aimed to identify the most effective strategies for accurate skin disease classification in dermoscopic images.
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