SE-DeepLabV3+: Cervical Cell Segmentation and Classification Using a Novel SE-Based DeepLabV3+ and Ensemble Method by Betelhem Zewdu Wubineh, Andrzej Rusiecki, Krzysztof Halawa

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Progress of fracture mapping technology based on CT three-dimensional reconstruction by Jichao Liu, Ziyan Zhang, Ji Qu, Chengdong Piao

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Multi-label Bird Species Classification Using Transfer Learning Network by Xue HAN, Jianxin PENG

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Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation by Hyemin Yoon, Do-Young Kang, Sangjin Kim

“…Our proposed method includes three modules: (i) a 3D teacher network that encodes volumetric prior knowledge from the 3D dataset, (ii) a 2D student network that encodes partial volumetric information from the 2D dataset, and aims to develop an understanding of the original volumetric imaging, and (iii) a distillation loss introduced to reduce the gap in the graph representation expressing the relationship between data in the feature embedding spaces of (i) and (ii), thereby enhancing the final performance. …”
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Prostate cancer classification using 3D deep learning and ultrasound video clips: a multicenter study by Wenjie Lou, Peizhe Chen, Chengyi Wu, Qinghua Liu, Lingyan Zhou, Lingyan Zhou, Maoliang Zhang, Jing Tu, Zhengbiao Hu, Cheng Lv, Jie Yang, Xiaoyang Qi, Xingbo Sun, Yanhong Du, Xueping Liu, Yuwang Zhou, Yuanzhen Liu, Yuanzhen Liu, Chen Chen, Chen Chen, Chen Chen, Zhengping Wang, Jincao Yao, Jincao Yao, Jincao Yao, Jincao Yao, Jincao Yao, Kai Wang

“…The development set comprised 552 men (median age: 71 years; IQR: 67–77 years), the internal test set included 93 men (median age: 71 years; IQR: 67–77 years), external test set 1 consisted of 96 men (median age: 70 years; IQR: 65–77 years), and external test set 2 had 74 men (median age: 72 years; IQR: 68–78 years). The I3D model achieved diagnostic classification AUCs greater than 0.86 in the internal test set as well as in the independent external test sets 1 and 2. …”
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MobileNetV3: an efficient deep learning-based feature selection and classification technique for cardiovascular disease by B. Dhanalaxmi, B. Naveen Kumar, Yeligeti Raju, Rama Seshagiri Rao Channapragada

Subjects: “…MobileNetV3…”
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Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier by Ranjana Agrawal, Sucheta Kulkarni, Madan Deshpande, Anita Gaikwad, Rahee Walambe, Ketan V. Kotecha

“…The fundus images were classified as without stage (Normal)/with Stage (ROP) by segmenting the ridge. Stages 1–3 were classified using machine Learning (ML) models. • This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. …”
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Segmentation-based deep 2D-3D multibranch learning approach for effective hyperspectral image classification by Tanver Ahmed, Adiba Mahjabin Nitu, Masud Ibn Afjal, Md Abdulla Al Mamun, Md Palash Uddin

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Evaluation of metal objects surface parameters informativity using 2D­ and 3D­data For classification of fractures by V. A. Ganchenko, E. E. Marushko, L. P. Podenok, A. V. Inyutin

“…This article describes evaluation the information content of metal objects surfaces for classification of fractures using 2D and 3D data. As parameters, the textural characteristics of Haralick, local binary patterns of pixels for 2D images, macrogeometric descriptors of metal objects digitized by a 3D scanner are considered. …”
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Self-Supervised Sensor Learning and Its Application: Building 3D Semantic Maps Using Terrain Classification by Chuho Yi, Donghui Song, Jungwon Cho

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D3GNN: Double dual dynamic graph neural network for multisource remote sensing data classification by Teng Yang, Song Xiao, Jiahui Qu

“…We propose a double dual dynamic graph neural network (D3GNN) with dynamic topological structure refinement for multisource RS data classification. …”
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Hybrid 3B Net and EfficientNetB2 Model for Multi-Class Brain Tumor Classification by R. Preetha, M. Jasmine Pemeena Priyadarsini, J. S. Nisha

“…We systematically compared multi-branch architectures, ranging from single to six branches, to identify the optimal configuration for accurate tumor classification. The three-branch network (3B Net) consistently yielded superior performance across multiple classification tasks. …”
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Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification by Herimanto, Arie Satia Dharma, Junita Amalia, David Largo, Christin Adelia Pratiwi Sihite

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Improving Fall Classification Accuracy of Multi-Input Models Using Three-Axis Accelerometer and Heart Rate Variability Data by Seunghui Kim, Jae Eun Ko, Seungbin Baek, Daechang Kim, Sungmin Kim

Subjects: “…three-axis acceleration sensor…”
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The comparison of three classification methods of remote sensing techniques for the purpose of categorizing habitat types of Khar Yamaat Nature Reserve by Munkhtur Davaagerel, Basan Munkhchuluun, Uudus Bayarsaikhan

“…Using Landsat satellite images from September 2014 and September 2019, the land cover change was classified in three ways using the pixel-based supervised classification method: Maximum likelihood classification, K-Nearest neighbor, and Minimum distance classifier. …”
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Features of abdominal delivery in obstetric institutions of level III by M. V. Galustyan, I. I. Kutsenko, I. O. Borovikov

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Classification system from Optical Coherence Tomography using transfer learning by Muhamad Asvial, Tobias Ivandito Margogo Silalahi, Muh. Asnoer Laagu

“…The purpose of model training, validation, and testing, the experiment uses 6,000 grayscale images labeled into four classes from the OCT data set. The Inception V3 model’s proposed additional layer exhibits an increase in accuracy of 3.08% and a reduction in the loss by 0.3767. …”
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Abaca Blend Fabric Classification Using Yolov8 Architecture by Cedrick D. Cinco, Leopoldo Malabanan R. Dominguez, Jocelyn F. Villaverde

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Statistical classification methods for estimating sex based on five skull traits: A nonmetric assessment using 3D CT models by Yun taek Shim, Deog-Im Kim, Nahyun Aum, Seung gyu Choi, Young Seok Lee, Hyung Nam Koo, Yi-Suk Kim

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On a Bayesian multivariate survival tree approach based on three frailty models by Patcharaporn Porndumnernsawat, Till D. Frank, Lily Ingsrisawang

“…The performance of a Bayesian multivariate survival tree approach based on shared gamma frailty models with Weibull distribution provided the highest accuracy. All three models, the accuracy tended to increase with an increase in the cluster size and the number of clusters. …”
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