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Ovaries are important parts of the female reproductive system because they produce the egg or ovum needed for fertilization. Cysts frequently impact female follicles, so torsion, infertility, and cancer can result from an enormous ovarian cyst. As a result, this is critical to get a diagnosis as quickly as feasible an ultrasound examination is performed to diagnose an ovarian cyst. So, this work gathered ultrasound images of different women's ovaries and determined whether or not an ovarian cyst was detected. This work proposed a novel technique to detect the ovarian cyst using images of ovarian ultrasound cysts from an ongoing database. Initially pre-processed by removing noise, followed by feature extraction, and finally classifying using new deep reinforcement learning with Harris Hawks Optimization (HHO) classifier. Automatic feature extraction is implemented using the recent popular convolutional neural network (CNN) technique that extracts image features as conditions in the reinforcement learning algorithm. Deep Q-Network (DQN) is generated to train a Q-network and detect the disease, and the swarm-based method of HHO utilizes the optimization method to produce optimal hyperparameters in the DQN model known as HHO-DQN. Extensive experimental evaluations on datasets show that the proposed HHO- DQN approach outperforms existing active learning approaches for ovarian cyst classification. [ABSTRACT FROM AUTHOR]

Though artificial intelligence (AI) has been used in nuclear medicine for more than 50 years, more progress has been made in deep learning (DL) and machine learning (ML), which have driven the development of new AI abilities in the field. ANNs are used in both deep learning and machine learning in nuclear medicine. Alternatively, if 3D convolutional neural network (CNN) is used, the inputs may be the actual images that are being analyzed, rather than a set of inputs. In nuclear medicine, artificial intelligence reimagines and reengineers the field's therapeutic and scientific capabilities. Understanding the concepts of 3D CNN and U-Net in the context of nuclear medicine provides for a deeper engagement with clinical and research applications, as well as the ability to troubleshoot problems when they emerge. Business analytics, risk assessment, quality assurance, and basic classifications are all examples of simple ML applications. General nuclear medicine, SPECT, PET, MRI, and CT may benefit from more advanced DL applications for classification, detection, localization, segmentation, quantification, and radiomic feature extraction utilizing 3D CNNs. An ANN may be used to analyze a small dataset at the same time as traditional statistical methods, as well as bigger datasets. Nuclear medicine's clinical and research practices have been largely unaffected by the introduction of artificial intelligence (AI). Clinical and research landscapes have been fundamentally altered by the advent of 3D CNN and U-Net applications. Nuclear medicine professionals must now have at least an elementary understanding of AI principles such as neural networks (ANNs) and convolutional neural networks (CNNs). [ABSTRACT FROM AUTHOR]

Land Use and Land Cover (LULC) classification is the process of locating and classifying regions of the Earth's surface (land cover) based on their physical attributes and human utilization (land use purpose). It is essential for mapping and monitoring changes in ecosystems, facilitating effective resource management and informed decision-making in land-use planning. Convolutional Neural Network (CNN's) is incapable of capturing long-range dependencies. The effectiveness of transformers relies on extensive training datasets, yet many satellite datasets have comparatively limited sample sizes. Moreover, due to the numerous factors influencing visual prominence, it is challenging to obtain abundant features from a single-color space. To overcome these limitations, we introduce an innovative bicolor architecture Downstream Lingering Attention Transformer Network (DsLATNet). DsLATNet characterizes remote sensing images on two color spaces viz RGB and HSV by processing the information using Residual Network −50(ResNet-50) backbone network.Extracted features of backbone network are further refined to acquire detailed aware features through Downstream Lingering Feature Pyramid Network (DsLFPN).This is further integrated with a DuoTransformer that utilizes attention mechanisms to obtain long-range dependencies. Eventually, the features are aggregated using residual connections that are dilated in-depth multiple times to produce class labels. The efficacy of the algorithm is evaluated through standard classification accuracy metrics. Experimental results on both the Wuhan Dense Labeling Dataset (WHDLD) and the Gaofen Image Dataset (GID) exhibit that the suggested technique exceeds cutting-edge classification methods. WHDLD achieved an Overall Accuracy (OA) of 86.34 %, Average Accuracy (AA) of 75.62 %, and Kappa coefficient (K) of 80.75, while the GID exhibited superior performance with an OA of 86.43 %, AA of 88.13 %, and K of 81.25. • We put forward bi-color integrated feature pyramid Duo Transformer network. • We integrate residual network with Duo transformer to capture local and global features to lessen gradient dispersion issues. • Feature pyramid attention module is incorporated to improve the aggregation of contextual details. • Transformer effectively merges bi-color features from lower to higher levels to enhance model's discriminative capability. [ABSTRACT FROM AUTHOR]

• Gray-Level Co-occurrence matrix and vantage point tree were used which has excellent performance and classifier. • Networks with two segmentation such as U-Net and 3D CNN are proposed for better and accurate predictions. • 3D CNN was used to segment the enhancing tumor more accurately. • U-Net was used in the tumor core for the better performance used in the sub-region of final prediction. Analysis and monitoring of disease development rely heavily on automated segmentation of brain tumors using MRI data. Because gliomas are aggressive and diverse, effective and precise segmentation techniques are utilized to divide tumors into intra-tumoral groups. In the proposed work, the Gray Level Co-occurrence Matrix feature extraction is used to extract characteristics from the image. In the segmentation problem of Brain tumor, Convolutional Neural Networks, which are widely employed in biomedical image segmentation, have greatly enhanced the state-of-the-art accuracy. In this work, we propose a major but simple combinative approach that results in improved and more precise estimates by combining two segmentation networks: a U-Net and a 3D CNN. For each model, it was assessed independently on the dataset to produce segmentation maps that varied greatly in terms of segmented tumor sub-regions and were then ensembled to reach the final prediction. On the validation set, the accuracy (percentage) of 98.29, 98.45, and 99.4 for tumor core, enhanced tumor and whole tumor, respectively, performed well in contrast to state-of-the-art designs presently existing. [Display omitted] [ABSTRACT FROM AUTHOR]