Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Bone Cancer Detection Based on Histopathology Images Using Optimized Temporal Spatial Multi Scale Dilated Convolutional Neural Network with MultiNet
The MultiNet presents a novel deep neural network framework designed for bone cancer detection by amalgamating features extracted from DenseNet-201, NASNetMobile, and VGG 16 models. Leveraging transfer learning and multi-scale feature fusion, the framework aims to improve the accuracy and efficiency of bone cancer diagnosis from microscopy images. In contrast to traditional manual diagnostic methods, which are costly and time-consuming, the study underscores the significance of Computer-Aided Diagnosis (CAD) systems in streamlining the detection process. The proposed MultiNet framework offers a reliable and expedited approach for identifying bone cancer cases, potentially revolutionizing diagnostic practices in the field. By integrating diverse features from different CNN models at various scales, the framework strives to provide precise and robust detection of bone cancer, ultimately enhancing diagnostic capabilities and potentially contributing to improved patient outcomes. The method currently being presented shows how important deep learning techniques are to improving the diagnosis and detection of bone cancer.
Bone Cancer Detection Based on Histopathology Images Using Optimized Temporal Spatial Multi Scale Dilated Convolutional Neural Network with MultiNet
The MultiNet presents a novel deep neural network framework designed for bone cancer detection by amalgamating features extracted from DenseNet-201, NASNetMobile, and VGG 16 models. Leveraging transfer learning and multi-scale feature fusion, the framework aims to improve the accuracy and efficiency of bone cancer diagnosis from microscopy images. In contrast to traditional manual diagnostic methods, which are costly and time-consuming, the study underscores the significance of Computer-Aided Diagnosis (CAD) systems in streamlining the detection process. The proposed MultiNet framework offers a reliable and expedited approach for identifying bone cancer cases, potentially revolutionizing diagnostic practices in the field. By integrating diverse features from different CNN models at various scales, the framework strives to provide precise and robust detection of bone cancer, ultimately enhancing diagnostic capabilities and potentially contributing to improved patient outcomes. The method currently being presented shows how important deep learning techniques are to improving the diagnosis and detection of bone cancer.
Bone Cancer Detection Based on Histopathology Images Using Optimized Temporal Spatial Multi Scale Dilated Convolutional Neural Network with MultiNet
Aarthy, R. (Autor:in) / Muthupriya, V. (Autor:in) / Kalpana, S (Autor:in) / Ashok, Ms Vidhya (Autor:in)
03.06.2024
575583 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch