In the automatic detection of suspicious shaded regions on CT images derived from the LIDC-IDRI dataset, the diagnostic system plays a significant role. This paper introduces an automatic recognition method for lung nodules of the regions of concern (ROI). The lung regions are segmented from DICOM image size 512 × 512 by adding a median filter, Gaussian filter, Gabor filter and watershed algorithm. AlexNet uses 227 × 227 × 3 with “fc7” (fully connected) layers and GoogLeNet uses 224 × 224 × 3 with “pool5-drop 7 × 7 s1” layers. Here, the authors explain what is better about AlexNet and GoogLeNet through its performance analysis, feature extraction, classification, sensitivity, specificity, detection and false alarm rate with time complexity. A multi-class SVM classifier with 100% precision and specificity provided the best performance in deep learning neural networks.

LC continues to be the most prevalent cause of cancer deaths worldwide, which calls for sophisticated detection strategies. The present study investigates 34 demographic, behavioral, and hematological risk factors based on a sample of 2,000 patient data records. A multi-model machine learning approach compares nine algorithms: KNN, AdaBoost (AB), logistic regression (LR), random forest (RF), SVM, naive Bayes (NB), decision tree (DT), gradient boosting (GB), and stochastic gradient descent (SGD). Performance measures (accuracy, sensitivity, specificity, F1-score, AUC) identify quantitative differences: GB had the best F1-scores (0.953) and NB had the second-best F1-score (0.945), while GB had the best sensitivity (99.1 %). The KNN-AB hybrid model reported the highest accuracy with 99.5 %, while RF reported the highest AUC with a value of 0.92. Ensemble approaches (RF, GB) showed robust predictive performance across measures through integration of complementary strengths of base models. Lasso and ridge regression were able to minimize overfitting, making them easier to interpret. Therapeutic uses include integration into electronic health records (EHRs) for computerized risk stratification, LC screening earlier, and public health interventions in high-risk subjects (smokers with abnormal hematologic markers). The research highlights the value of hybrid ML models to integrate behavioral and biological data to effectively predict LC. Subsequent work can expand predictive capabilities through imaging data and genomics data incorporation, and continue to advance early identification and patient-specific therapy options. This is an intersection of computational advances and clinical translation, providing scalable solutions for global LC diagnosis.

In this report, we propose an alternative way of detecting the Covid-19 disease using Convolutional Neural Networks based deep learning models which will classify between Covid-19 and other similar respiratory diseases such as other types of Viral Pneumonia or Bacterial Pneumonia, using images of Chest X-Rays. The models developed as part of this project achieved approximately 98% accuracy on a test dataset of 300 images.

Managing personal finance is an important aspect of modern life that requires units that provide accurate and real-time insight. Traditional economic tracking methods, such as manual accounting and spreadsheets, often give rise to disability and mistakes. This article presents the development of a used tracker application when using Mern (MongoDB, Express.JS, React and Node.JS) Stack, which provides a simple and interactive solution for tracking income, expenses and budget. The application allows users to record and classify transactions, imagine financial data through reports and graphs and determine budget goals. Backend, Node.JS and Express-Powered, Effective Data Management and API administration ensure management, while MongoDB offers a flexible and scalable database. The response-based limit increases the user experience with an intuitive dashboard and financial insight into real time. Safety is an important focus, where JWT has been used on certification and encryption of user data protection. It examines architecture, main functions, challenges and safety aspects of the paper system, as well as possible promotion such as AI-operated expenditure spread, bank integration and blockchainbased security. The expense-tracker application aims to have a scalable, efficient and secure solution, which helps individuals to manage their finances more efficiently and make informed financial decisions.