Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F1–Score, and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.

/pdf/hybrid-model-for-detection-of-cervical-cancer-using-causal-17pr22fs.pdf

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques

FormerLeaf: An efficient vision transformer for Cassava Leaf Disease detection

Healthcare, one of the most important industries, is data-oriented, but most of the research in this industry focuses on incorporating the internet of things (IoT) or connecting medical equipment. Very few researchers are looking at the data generated in the healthcare industry. Data are very important tools in this competitive world, as they can be integrated with artificial intelligence (AI) to promote sustainability. Healthcare data include the health records of patients, drug-related data, clinical trials data, data from various medical equipment, etc. Most of the data management processes are manual, time-consuming, and error-prone. Even then, different healthcare industries do not trust each other to share and collaborate on data. Distributed ledger technology is being used for innovations in different sectors including healthcare. This technology can be incorporated to maintain and exchange data between different healthcare organizations, such as hospitals, insurance companies, laboratories, pharmacies, etc. Various attributes of this technology, such as its immutability, transparency, provenance etc., can bring trust and security to the domain of the healthcare sector. In this paper, a decentralized access control model is proposed to enable the secure interoperability of different healthcare organizations. This model uses the Ethereum blockchain for its implementation. This model interfaces patients, doctors, chemists, and insurance companies, empowering the consistent and secure exchange of data. The major concerns are maintaining a history of the transactions and avoiding unauthorized updates in health records. Any transaction that changes the state of the data is reflected in the distributed ledger and can be easily traced with this model. Only authorized entities can access their respective data. Even the administrator will not be able to modify any medical records.

/pdf/blockchain-technology-and-artificial-intelligence-based-2rhs7abw.pdf

Blockchain Technology and Artificial Intelligence Based Decentralized Access Control Model to Enable Secure Interoperability for Healthcare

Image thresholding approaches for medical image segmentation - short literature review

Cassava is a vital sustenance crop for millions of individuals around the globe, especially in developing nations. However, cassava production is threatened by various leaf maladies that can drastically reduce productivity. Effective management and prevention of these diseases require early detection and diagnosis. This study devised a federated learning-based Convolutional Neural Network (CNN) model for the classification of five cassava leaf disease classes: Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM), Cassava Mosaic Disease (CMD), and Healthy. The proposed model showed promising performance on a dataset of 9,465 images, with precision values between 0.74 and 0.85, recall values between 0.70 and 0.88, and overall accuracy of 78.6%. This study's federated learning approach offers several advantages, including data privacy, scalability, and continuous learning, making it suitable for real-world cassava leaf disease classification applications. In addition, this study demonstrates the potential for utilizing advanced machine learning techniques in the agricultural sector to improve early disease detection and support sustainable farming practices.

Decentralized Detection of Cassava Leaf Diseases: A Federated Convolutional Neural Network Solution

Cassava is a crucial food and nutrition security crop cultivated by small-scale farmers because it can survive in a brutal environment. It is a significant source of carbohydrates in African countries. Sometimes, Cassava crops can be infected by leaf diseases, affecting the overall production and reducing farmers’ income. The existing Cassava disease research encounters several challenges, such as poor detection rate, higher processing time, and poor accuracy. This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN). The existing Standard CNN model utilizes extensive data processing features, increasing the computational overhead. A depth-wise separable convolution layer is utilized to resolve CNN issues in the proposed ECNN model. This feature minimizes the feature count and computational overhead. The proposed ECNN model utilizes a distinct block processing feature to process the imbalanced images. To resolve the color segregation issue, the proposed ECNN model uses a Gamma correction feature. To decrease the variable selection process and increase the computational efficiency, the proposed ECNN model uses global average election polling with batch normalization. An experimental analysis is performed over an online Cassava image dataset containing 6256 images of Cassava leaves with five disease classes. The dataset classes are as follows: class 0: “Cassava Bacterial Blight (CBB)”; class 1: “Cassava Brown Streak Disease (CBSD)”; class 2: “Cassava Green Mottle (CGM)”; class 3: “Cassava Mosaic Disease (CMD)”; and class 4: “Healthy”. Various performance measuring parameters, i.e., precision, recall, measure, and accuracy, are calculated for existing Standard CNN and the proposed ECNN model. The proposed ECNN classifier significantly outperforms and achieves 99.3% accuracy for the balanced dataset. The test findings prove that applying a balanced database of images improves classification performance.

/pdf/enhanced-convolutional-neural-network-model-for-cassava-leaf-3gxm4948.pdf

Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification

In this paper, a disease prediction system has been designed that takes the symptoms entered by an individual as input and shows the predicted output i.e. the most probable disease to them. Random Forest Classifier algorithm is being used in the backend for prediction purposes. The dataset that is being used consists of 132 symptoms that are linked to 41 diseases. In addition, the system could also suggest precautions and medicines to the user based on their disease. This can minimize the efforts and time invested by the doctors and patients by automatizing the process.

Disease Predictor Using Random Forest Classifier

An integrated approach for breast cancer classification

The gold standard for fault identification in the casting process is the human visual inspection though that includes human error and it is time-consuming. In this work, to automate the inspection process in casting production three deep learning-based artificial intelligence systems for detecting manufacturing flaws in submersible pump impellers are created. The suggested casting fault detectors intend to create a system for fault identification in casting goods that are automated, reliable, and accurate. Here on the top view image data of submersible pump impellers, two transfer learning-based convolution neural networks and one lightly structured convolution neural network are created and verified. The performance of the suggested models is measured in terms of accuracy, sensitivity, precision, and f1-score, and then compared with the state-of-the-art works. The best performance among the proposed casting fault detection systems are 99.88% accuracy, 100% precision, 99.77% recall, and 99.88% f1-score in binary classification and this outperformed previous related work.

Casting Fault Detection by Deep Convolution Neural Networks

With the rise of attention to healthcare since the start of the century, which the recent pandemic has emphasized, the number of hospitals and clinics has increased exponentially. The growth in hospitals and patients has also resulted in increased medical waste. The different kinds of medical waste must be segregated and disposed of properly to prevent the spread of bacteria and viruses and cross-contamination. However, it is not economically feasible to hire a workforce that can segregate said waste. With the perceived popularity of deep learning and image classification systems, creating a Deep Learning model to categorize the different kinds of medical waste is possible. Hence using a deep learning-based classification method in which an appropriate pre-trained model is selected for practical implementation, followed by transfer learning methods to improve classification results, is appropriate. Different types of medical waste are grouped into umbrella categories(general, hazardous, infectious). An ideal situation would be where images are uploaded, and the machine can classify the presented waste appropriately with little to no waiting times. Three out of four of the modified pre-trained models with different architectures were able to achieve an accuracy above 95 percent.

Medical Waste Classification using Deep Learning and Convolutional Neural Networks

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