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The hippocampal region is one of the most affected brain areas observed as a landmark in Magnetic Resonance Imaging (MRI) images for Alzheimer’s disease (AD) diagnosis. The diminished alterations in the hippocampal and degeneration of cholinergic circuits have been conclusively correlated with a decline in memory and cognitive function. However, the hippocampal region may not appear as clearly defined as other brain regions, making it difficult for neurologists and researchers to identify by visual inspection. The application of deep learning models to pinpoint the hippocampal region was initially valued. We assessed the ability of a deep learning model, You Only Live Once (YOLO), to detect hippocampal regions in three MRI image views and categories. The Alzheimer’s Disease Neuroimaging Initiative-first (ADNI−1) dataset was used with 220 subjects in three categories using the three YOLO models. We obtained the YOLO performance for hippocampal region detection with accuracy in three views and categories. The average mean Average Precision (mAP) performance accuracy for YOLOv3 was 0.87, YOLOv4 was 0.85, and YOLOv5 was 0.96, respectively. The high accuracy of the detection of the hippocampal region was remarkable. We found that the sagittal view was higher than the axial and coronal views. Simultaneously, the Mild Cognitive Impairment (MCI) in the coronal view was lower among the three models. The results showed that YOLOv5 is a suitable model for detecting the hippocampal region in MRI images, and the sagittal view is the most reliable for detecting the hippocampal region in diagnosing AD. Our findings demonstrate the importance of detecting the hippocampal region to diagnose AD and accurately analyzing the hippocampal area within the region. The YOLOv5 model substantially affected performance metrics and interpretability across the three views and categories.

Background: In magnetic resonance imaging (MRI), lumbar disc herniation (LDH) detection is challenging due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. Lumbar abnormalities in MRI can be detected automatically by using deep learning methods. As deep learning models gain recognition, they may assist in diagnosing LDH with MRI images and provide initial interpretation in clinical settings. YOU ONLY LOOK ONCE (YOLO) model series are often used to train deep learning algorithms for real-time biomedical image detection and prediction. This study aims to confirm which YOLO models (YOLOv5, YOLOv6, and YOLOv7) perform well in detecting LDH in different regions of the lumbar intervertebral disc.Materials and methods: The methodology involves several steps, including converting DICOM images to JPEG, reviewing and selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on the dataset. The training dataset was combined with the radiologist’s labeling and annotation, and then the deep learning models were trained using the training/validation dataset.Results: Our result showed that the 550-dataset with augmentation (AUG) or without augmentation (non-AUG) in YOLOv5x generates satisfactory training performance in LDH detection. The AUG dataset overall performance provides slightly higher accuracy than the non-AUG. YOLOv5x showed the highest performance with 89.30% mAP compared to YOLOv6, and YOLOv7. Also, YOLOv5x in non-AUG dataset showed the balance LDH region detections in L2-L3, L3-L4, L4-L5, and L5-S1 with above 90%. And this illustrates the competitiveness of using non-AUG dataset to detect LDH.Conclusion: Using YOLOv5x and the 550 augmented dataset, LDH can be detected with promising both in non-AUG and AUG dataset. By utilizing the most appropriate YOLO model, clinicians have a greater chance of diagnosing LDH early and preventing adverse effects for their patients.

Alzheimer’s disease (AD) is a major public health priority. Hippocampus is one of the most affected areas of the brain and is easily accessible as a biomarker using MRI images in machine learning for diagnosing AD. In machine learning, using entire MRI image slices showed lower accuracy for AD classification. We present the select slices method by landmarks on the hippocampus region in MRI images. This study aims to see which views of MRI images have higher accuracy for AD classification. Then, to get the value of three views and categories, we used multiclass classification with the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using Resnet50 and LeNet. The models were used in a total dataset of 4,500 MRI slices in three views and categories. Our study demonstrated that the selecting slices performed better than using entire slices in MRI images for AD classification. Our method improves the accuracy of machine learning, and the coronal view showed higher accuracy. This method played a significant role in improving the accuracy of machine learning performance. The results for the coronal view were similar to the medical experts usually used to diagnose AD. We also found that LeNet models became the potential model for AD classification.

Various culture in Indonesia is slowly being forgotten by the majority, for people now uses another way of communication to deliver the message from one person to another. One of the examples that could see is the usage of Javanese script or usually called Hanacaraka. Therefore, studying the visual language of shoe design from Flymax Indonesia case study will help the public to understand how urgent creative cultural product is. This research uses qualitative research regarding the case study based on the in-depth interview through the owner of Flymax Indonesia and visual language analysis regarding the visuals. This study has implications for the creative industry practitioners in conveying visual language and impacting methods in developing creative products by including cultural elements in popular products. From Flymax study case, implementing cultural to modern shoes is to strengthen cultural identity, user identity, and local brand identity, moreover supporting local economic growth and cultural preservation.

People with cerebral palsy (CP) suffer primarily from lower-limb impairments. These impairments contribute to the abnormal performance of functional activities and ambulation. Footprints, such as plantar pressure images, are usually used to assess functional performance in people with spastic CP. Detecting left and right feet based on footprints in people with CP is a challenge due to abnormal foot progression angle and abnormal footprint patterns. Identifying left and right foot profiles in people with CP is essential to provide information on the foot orthosis, walking problems, index gait patterns, and determination of the dominant limb. Deep learning with object detection can localize and classify the object more precisely on the abnormal foot progression angle and complex footprints associated with spastic CP. This study proposes a new object detection model to auto-determine left and right footprints. The footprint images successfully represented the left and right feet with high accuracy in object detection. YOLOv4 more successfully detected the left and right feet using footprint images compared to other object detection models. YOLOv4 reached over 99.00% in various metric performances. Furthermore, detection of the right foot (majority of people’s dominant leg) was more accurate than that of the left foot (majority of people’s non-dominant leg) in different object detection models.

Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

Abstrak Poster merupakan salah satu media populer dalam desain komunikasi visual, poster juga marak sebagai media utama dalam berbagai macam pergerakan. Namun poster hanyalah suatu media, pesan dalam poster tersebut yang sebenarnya perlu menjadi perhatian. Selain iklan komersial poster juga sering digunakan sebagai iklan layanan masyarakat. Poster-poster Iklan layanan Masyarakat tersebut bertujuan untuk mengubah perilaku masyarakat melalui pesan yang ingin disampaikan oleh desainer poster tersebut. Salah satunya adalah poster Marlyn Fish Deep 2009 karya Sigit Setiawan dan Aditya Donny Suryawan. Karya tersebut berhasil menjuarai salah satu kompetisi poster bertajuk Deep Indonesia 2009, tujuan poster ini untuk mengubah perilaku masyarakat untuk lebih memperhatikan dan menjaga kelestarian terumbu karang di Indonesia. Penelitian ini bertujuan untuk membahas poster menggunakan analisis tinjauan desain melalui empat tahap yakni, tahap deskripsi, tahap analisa, tahap interpretasi, dan tahap evaluasi. Tinjauan desain tersebut diharapkan dapat menjadi jembatan bagi pembaca dalam mengartikan karya poster tersebut secara lebih terperinci. Kata Kunci: Poster, Tinjauan Desain, Iklan Layanan Masyarakat. Abstract Poster is one of the popular media in visual communication design, poster also rife as the main media in a wide range of movement. But the poster is just a medium, the message in the poster that actually need to be a concern. Besides commercial advertising posters are also frequently used as public service advertising. Posters of the public service ads aimed at changing people's behavior through the message to be conveyed by the poster designer. One of them is the poster Marlyn Fish Deep 2009 by Sigit Setiawan and Aditya Donny Suryawan. The art work succesfull to the first winner on poster competition titled Deep Indonesia 2009, the purpose of the poster is to change the behavior of the public to pay more attention and to preserve the coral reefs in Indonesia. This study aims to discuss the poster using analytical design review through four stages namely, stage descriptions, the analysis stage, the stage of interpretation and evaluation phase. The design review is expected to be a bridge for the reader in interpreting the poster works in more detail. Keywords: Poster, Design Review, Public Service Ads

Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness of deep learning results. From 2018 to 2020, deep learning has the improved solutions used to overcome these challenges; however, deep learning solutions for ultrasound images have not been compared to the conditions and strategies used to comprehend the current state of knowledge for handling skeletal and smooth muscle ultrasound images. This study aims to look at the challenges and trends of deep learning performance, especially in regard to overcoming muscle ultrasound image problems such as low image quality, muscle movement in skeletal muscles, and muscle thickness in smooth muscles. Skeletal muscle segmentation presents difficulties due to the regular movement of muscles and resulting noise, recording data through skipped connections, and modified layers required for upsampling. In skeletal muscle classification, the problems faced are area-specific, thus making a cropping strategy useful. Furthermore, there is no need to add additional layer modifications for smooth muscle segmentation as muscle thickness is the main problem in such cases.

Typography is an element of study in visual communication design related to letter. One of the most uniqueapplications of typography is the combination of letter with sound (onomatopoeia). Typography of onomatopoeiain visual communication design can be found in comic. The font with its own characteristic is combined withthe sound effect and then visualized as a part of text in comic “Tigan Ngasak Batavia†by Bengkel QomikSurakarta. The problem discussed in this research is how the role of typography onomatopoeia in comicanalyzed through sound classification and semiotic approach of Roland Barthes that is denotative andconnotative. The method of qualitative research used includes interview, library study, and document. Theresult of research is that typography onomatopoeia has a function as the story determiner. The dividing ofsound source classification and sound effect in comic is completely spread. Not all of the sound effectpresented in comic “Tigan Ngasak Batavia†shows the original sound. It exactly becomes the attractivenessand stabilization of comedy when the conflict takes place. Typography onomatopoeia in “Tigan NgasakBatavia†has a very important function that is helping audience to hear by eyes. It shows the proof that text incomic has a strong power in conveying messages.Keywords: typography, onomatopoeia, comic, visual communication