[Retracted] Effective CBMIR System Using Hybrid Features‐Based Independent Condensed Nearest Neighbor Model
HD Praveena, NS Guptha… - Journal of …, 2022 - Wiley Online Library
In recent times, a large number of medical images are generated, due to the evolution of
digital imaging modalities and computer vision application. Due to variation in the shape …
digital imaging modalities and computer vision application. Due to variation in the shape …
Computer-aided diagnosis through medical image retrieval in radiology
W Silva, T Gonçalves, K Härmä, E Schröder… - Scientific reports, 2022 - nature.com
Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and
consequently, to undesired diagnosis mistakes. Decision support systems can be used to …
consequently, to undesired diagnosis mistakes. Decision support systems can be used to …
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
According to the Global Cancer Observatory, 2020, breast cancer is the most prevalent
cancer type in both genders (11.7%), while prostate cancer is the second most common …
cancer type in both genders (11.7%), while prostate cancer is the second most common …
Residual block convolutional auto encoder in content-based medical image retrieval
Approximately 12 percent of men will suffer from prostate cancer during their lifetime. While
some types of this cancer grow slowly and need minimal treatment, others are aggressive …
some types of this cancer grow slowly and need minimal treatment, others are aggressive …
Wwfedcbmir: world-wide federated content-based medical image retrieval
The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool
that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical …
that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical …
Dimension reduction using dual-featured auto-encoder for the histological classification of human lungs tissues
Histopathology images are visual representations of tissue samples that have been
processed and examined under a microscope in order to establish diagnoses for various …
processed and examined under a microscope in order to establish diagnoses for various …
Convolution Neural Network Approaches for Cancer Cell Image Classification
Recently, research incorporating the benefits of deep learning in the application of cancer
cell classification and analysis has been actively conducted. In this paper, we investigated …
cell classification and analysis has been actively conducted. In this paper, we investigated …
A survey on machine learning enhanced integrated sensing and communication systems: Architectures, algorithms, and applications
MAK Respati, BM Lee - IEEE Access, 2024 - ieeexplore.ieee.org
Integrated sensing and communications (ISAC) technology is being developed in wireless
communications systems in the sixth generation (6G). ISAC has the advantages of lower …
communications systems in the sixth generation (6G). ISAC has the advantages of lower …
Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor
AE Minarno, MY Hasanuddin, Y Azhar - JOIV: International Journal on …, 2023 - joiv.org
Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik
motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is …
motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is …
Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging
Z Tabatabaei, A Colomer, JAO Moll… - arXiv preprint arXiv …, 2024 - arxiv.org
Computer Aid Diagnosis (CAD) has developed digital pathology with Deep Learning (DL)-
based tools to assist pathologists in decision-making. Content-Based Histopathological …
based tools to assist pathologists in decision-making. Content-Based Histopathological …