Developing a breast cancer screening method is very important to facilitate early breast cancer detection and treatment. Building a screening method using medical imaging modality that does not cause body tissue damage (non-invasive) and does not involve physical touch is challenging. Thermography, a non-invasive and non-contact cancer screening method, can detect tumors at an early stage even under precancerous conditions by observing temperature distribution in both breasts. The thermograms obtained on thermography can be interpreted using deep learning models such as convolutional neural networks (CNNs). CNNs can automatically classify breast thermograms into categories such as normal and abnormal. Despite their demostrated utility, CNNs have not been widely used in breast thermogram classification. In this study, we aimed to summarize the current work and progress in breast cancer detection based on thermography and CNNs. We first discuss of breast thermography potential in early breast cancer detection, providing an overview of the availability of breast thermal datasets together with publicly accessible. We also discuss characteristics of breast thermograms and the differences between healthy and cancerous thermographic patterns. Breast thermogram classification using a CNN model is described step by step including a simulation example illustrating feature learning. We cover most research related to the implementation of deep neural networks for breast thermogram classification and propose future research directions for developing representative datasets, feeding the segmented image, assigning a good kernel, and building a lightweight CNN model to improve CNN performance. INDEX TERMS breast cancer; convolutional neural network; deep learning; early detection; thermogram

Objective Routine dialysis is stressful. It has the possibility of leading to depression and anxiety and also reducing patients’ quality of life. Despite these significant consequences, these comorbidities have been rarely studied among Indonesian patients. This study aims to examine the rate of depression, anxiety, and the role of acceptance of their illness on patients’ quality of life. Method A total of 213 patients undergoing hemodialysis in three general hospitals in Aceh, Indonesia, were included in the study. The presence of depression, anxiety, and the quality of life of each patient was assessed using the hospital anxiety and depression scale and WHO quality of life-BREF questionnaires. Results The prevalence of depression and anxiety was 46% and 30.5%, respectively. Depression was only associated with the presence of anxiety and the duration of hemodialysis. Anxiety was negatively associated with quality of life but positively associated with depression and the prescription of an anxiolytic. Overall quality of life was associated with age, body mass index, the presence of anxiety, and acceptance of the illness. Acceptance of the illness was also independently associated with almost every domain of patients’ quality of life. Conclusions The rates of depression and anxiety among patients undergoing hemodialysis in the current study setting are relatively similar to the rates in other settings. Patients’ acceptance of their illnesses is significantly associated with the occurrence of anxiety and quality of life. Therefore, health practitioners should help patients accept their illnesses and the administration of regular hemodialysis.

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The majority of catches by fishermen in Aceh, Indonesia are sold raw, directly to consumers, and in the local market. This contributes to the low price of fish and low income for the fishermen, and the COVID-19 outbreak has made this situation even worse. One solution could be the establishment of a cold storage business in the area. This study assessed the financial feasibility of a 200-ton cold storage business in Banda Aceh, the capital of Aceh province. Using secondary data collected from online sources, we applied the most common financial indicators used in feasibility studies, namely Net Present Value (NPV), Internal Rate of Return (IRR), and Benefit-Cost Ratio (BCR). A sensitivity test was also performed to predict the feasibility of the cold storage business if the basic assumptions are changed. We found that cold storage in Banda Aceh is financially feasible as the NPV was positive, the IRR was higher than the interest rate (i.e., the discount rate), and the BCR was higher than 1. Besides, the sensitivity test also suggested that cold storage would still be feasible even if there were a 20% increase or decrease in storage capacity and project costs.

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