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One of the most pressing issues in the current COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest possible cost and during the disease's early stages. Deep learning-based models have the potential to provide an accurate and efficient approach for the identificati...
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The use and the production of sanitizers have increased in the post-pandemic situation to prevent the further spread of COVID-19. Usability assessment of sanitizer containers is essentially required to evaluate the effectiveness, efficiency, and satisfactory use of the sanitizer containers. This study aimed to evaluate the system usability scale (S...
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... In contrast to manual visual interpretation, artificial intelligence (AI), DL algorithms, and ML have demonstrated greater performance in the diagnosis of diseases just as COVID-19, Breast cancer, kidney disease, and diabetic retinopathy [2][20][21] [22]. Several data augmentation techniques were used to create additional data with variations and kidney dimension observations by cutting out the kidneys' periphery from ultrasound pictures in order to retrieve the information [23]. ...
Chronic kidney disease (CKD), a consequential health issue that can deeply affect an individual's overall wellness, can be initiated by either kidney cancer or a gradual reduction in kidney function. As the chronic disease advances, it can reach a critical stage where only dialysis or surgery can save lives. Halting its progress becomes crucial. CKD patients also face a heightened risk of premature death. Early detection of associated conditions poses a challenging task for healthcare professionals aiming to prevent their onset. A unique deep learning model is presented in this work for the prediction of CKD. Many existing CKD prediction models have the drawbacks of producing less accuracy, mispredicting, utilizing more computation time, and using low-quality datasets or data with noise and missing values, leading to misprediction. So it is necessary to develop new techniques that give high predictions with less computation time. The objective of this research work is to build improved ResNet models for the prediction of chronic kidney disease and evaluate their performance in comparison to other cutting-edge machine learning and deep learning methods. This research work developed ResNet models such as improved ResNet 152v2 with inception, improved ResNet 101, and improved ResNet50 models that produced 99.90%, 96.53%, and 93.968% accuracy, respectively. The proposed ResNet models for CKD prediction will be useful to nephrologists and other medical professionals.
... It is a manually curated internet database listing more than 90 plant image analysis programmes. COVID-19 x-ray images are used in COVID disease prediction and detection [11].The website plant-image-analysis.org displays each programme in a consistent and succinct way, making it easy for users to find the options that best suit their experimental requirements. Additionally, the website allows for user reviews, criticism, and submissions of new software [12] . ...
... The need for each additional examination should still be determined on its own merits if a person has already had a significant number of X-rays and the cumulative risk is of concern [4]. CNN is widely used in efficient feature extraction and prediction of various diseases [5][6] [7]. Around the world, pneumonia is a leading cause of death for those over the age of 65 and young children under the age of 5. ...
The lungs play a crucial role as the primary components of the human respiratory system, making them susceptible to inflammation and impact lesions in our daily lives. Among all infections, pneumonia holds the distinction of being the most widespread worldwide, with the lungs serving as the gateway for its spread throughout the body. In hospital settings, chest X-rays emerge as the most prevalent diagnostic tool employed to accurately identify pneumonia. Physicians heavily rely on these X-ray images to make precise diagnoses and monitor the progress of pneumonia treatment. Moreover, this type of chest X-ray facilitates the detection of other conditions like emphysema, lung cancer, the positioning of lines and tubes, and tuberculosis. The challenges faced by the existing deep learning models for pneumonia prediction include high computational complexity, prolonged model training times, and a lack of efficient preprocessing techniques. These issues contribute to misdiagnosis and inaccurate predictions of pneumonia. Moreover, the lack of interpretability in many of these models further hinders their acceptance and understanding in clinical applications. This research aims to tackle the challenges presented by current techniques by proposing a customized ResNet152v2 deep learning model. The primary objective is to design and deploy this modified ResNet152v2 model for pneumonia prediction from chest X-rays, achieving high accuracy while minimizing computational complexity and reducing computation time. This model outperformed well when compared with the existing methods and produced accuracy of 99.77%, Sensitivity of 99.86%, specificity of 95.4%, and precision of 99.86%.
Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images.
Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images
