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Journal : Journal of Electronics, Electromedical Engineering, and Medical Informatics

Model Group Decision Support System Based on Depression Anxiety Stress Scales Using Ordered Weighted Averaging Aggregation Method Wiharto, Wiharto; Putri, Della K.; Sihwi, Sari W.; Salamah, Umi; Suryani, Esti; Atina, Vihi; Utomo, Pradityo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.678

Abstract

Depression, anxiety, and stress are common psychological conditions often triggered by the pressures of daily life. Depression Anxiety Stress Scale (DASS), is a widely used tool for assessing the severity of these disorders, available in different versions such as the DASS-21 and DASS-42. In line with these findings, DASS-21 consists of 21 symptom items, categorized into three types of disorders, with seven items assigned to each. In contrast, the DASS-42 includes 42 symptom items, with 14 items allocated per disorder. Both versions serve as standardized tools for assessing the severity of depression, anxiety, and stress, and the different versions show that one item only affects one disorder. In practice, it can affect several disorders with different priorities. This condition increases the risk of subjective bias in a psychologist's decision-making, as personal experiences and perceptions may influence their assessments. Therefore, this study aims to develop a Group Decision Support System (GDSS) model that considers the preferences of several psychologists in determining the priority of disorders based on the DASS-42 and DASS-21 items. The model has been built using the psychologist's preference method for DASS-42 and DASS-21 in fuzzy form, then combined using the Ordered Weighted Averaging (OWA) method to produce one decision. The alignment of top-priority items between GDSS and DASS was assessed as part of the evaluation. The results show a high degree of similarity, with GDSS matching 16 out of 21 symptom items in DASS-21 and 35 out of 42 items in DASS-42. The GDSS model can accommodate the preferences of decision-makers in providing weighting of the influence on each item in the DASS-21 and DASS-42, thereby providing more objective decisions.
Breast Cancer Classification on Ultrasound Images Using DenseNet Framework with Attention Mechanism Azka, Hanina Nafisa; Wiharto, Wiharto; Suryani, Esti
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.779

Abstract

Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Early detection of breast cancer being critical for increasing survival rates. Ultrasound image is commonly used for breast cancer screening due to its non-invasive, safe, and cost-effective. However, ultrasound images are often of low quality and have significant noise, which can hinder the effectiveness of classification models. This study proposes an enhanced breast cancer classification model that leverages transfer learning in combination with attention mechanisms to improve diagnostic performance. The main contribution of this research is the introduction of Dense-SASE, a novel architecture that combines DenseNet-121 with two powerful attention modules: Scaled-Dot Product Attention and Squeeze-and-Excitation (SE) Block. These mechanisms are integrated to improve feature representation and allow the model to focus on the most relevant regions of the ultrasound images. The proposed method was evaluated on a publicly available breast ultrasound image dataset, with classification performed across three categories: normal, benign, and malignant. Experimental results demonstrate that the Dense-SASE model achieves an accuracy of 98.29%, a precision of 97.97%, a recall of 98.98%, and an F1-score of 98.44%. Additionally, Grad-CAM visualizations demonstrated the model's capability to localize lesion areas effectively, avoiding non-informative regions, and confirming the model's interpretability. In conclusion, the Dense-SASE model significantly improves the accuracy and reliability of breast cancer classification in ultrasound images. By effectively learning and focusing on clinically relevant features, this approach offers a promising solution for computer-aided diagnosis (CAD) systems and has the potential to assist radiologists in early and accurate breast cancer detection.