2D Floor Plan Segmentation Based on Down-sampling

In recent years, floor plan segmentation has gained significant attention due to its wide range of applications in floor plan reconstruction and robotics. In this paper, we propose a novel 2D floor plan segmentation technique based on a down-sampling approach. Our method employs continuous down-sampling on a floor plan to maintain its structural information while reducing its complexity. We demonstrate the effectiveness of our approach by presenting results obtained from both cluttered floor plans generated by a vacuum cleaning robot in unknown environments and a benchmark of floor plans. Our technique considerably reduces the computational and implementation complexity of floor plan segmentation, making it more suitable for real-world applications. Additionally, we discuss the appropriate metric for evaluating segmentation results. Overall, our approach yields promising results for 2D floor plan segmentation in cluttered environments

Rare case of recurrent Kikuchi-Fujimoto disease

Kikuchi-Fujimoto disease (KFD), or histiocytic necrotizing lymphadenitis, is a rare benign, self-limiting cervical lymphadenitis of unknown etiology. It closely mimics infective and immunological disorders. Here a rare case of recurrent KFD in a 13-year-old girl is presented. This girl presented with low grade fever at night times with sweating of 20 days duration and with painful right cervical lymphadenopathy. She had a bad history of recurrent episodes of lymphadenopathy seven years ago again after two years ago she was treated with empirical ATT. Her routine blood and urine tests were normal. Tests for infection and autoimmunity were negative. Histopathological examination is suggestive of Kikuchi lymphadenitis. Thus, she was diagnosed as a case of recurrent Kikuchi and was treated with oral steroids and hydroxychloroquine (HCQ). To the best of our knowledge, this is the first case of recurrent Kikuchi observed in an Indian patient and treated with Hydroxychloroquine and low dose steroids

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN)

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, the authors propose a technique to classify Parkinson’s disease by MRI brain images. Initially, the input data is normalized using the min-max normalization method, and then noise is removed from the input images using a median filter. The Binary Dragonfly algorithm is then used to select features. In addition, the Dense-UNet technique is used to segment the diseased part from brain MRI images. The disease is then classified as Parkinson's disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with the Enhanced Whale Optimization Algorithm (EWOA) to achieve better classification accuracy. In this work, the Parkinson's Progression Marker Initiative (PPMI) public dataset for Parkinson's MRI images is used. Indicators of accuracy, sensitivity, specificity and precision are used with manually collected data to evaluate the effectiveness of the proposed methodology