Lung cancer is one of the most prevalent illnesses in the world and a leading contributor to rising death rates. Early detection of cancer enables treatment. Early-stage lung cancer classification is difficult because of varied clinical presentations, leading to increased processing time and resource demands for clinicians. Deep learning techniques have been used extensively in a number of medical professions in recent years to improve early diagnosis and treatment stages. In cases of various malignant tumors, such as lung cancer, when time is of the essence for promptly recognizing the patient's condition. A novel deep learning-based LUNCERDEN model has been presented for lung cancer classification in order to address these issues. Lung cancer can be detected from CT scans using this process. Initially the input image CT is gathered from the available datasets. Adaptive median filters are used as a preprocessing technique to lower noise and enhance input image quality. For feature extraction, the preprocessed image is fed An arrhythmia is a disorder where the heartbeat is very fast or irregular. In the medical field, detecting arrhythmias is among the most challenging tasks. Arrhythmia detection is extremely challenging due to the volume and complexity of ECG data involved. Identifying arrhythmias by traditional methods requires a lot of effort and money. For the purpose of addressing these challenges, the researchers propose an advanced deep learning-based approach, known as AD-BRU, with features specifically designed for identifying arrhythmias through ECG data. Utilizing a variety of datasets derived from the MIT-BIH database, the approach can be applied to a wide range of problems. Data quality is enhanced by preprocessing the ECG input images with a discrete wavelet integrated filter. A primary objective of this study is to create and test an AD-BRU model that can detect arrhythmias effectively. Several metrics, such as precision, F1 score, specificity, recall, and accuracy, are used to evaluate the model's performance. Compared to Wavelet transformation, LSTM deep learning, and EnsCVDD, respectively, the suggested AD-BRU increases overall accuracy by 0.36%, 5.42%, and 10.98%. The results demonstrate that the AD–BRU approach significantly outperforms previous methods, achieving an average accuracy of 98.86%. According to the study's findings, the AD-BRU model provides a more precise and effective method for detecting arrhythmias, which could increase the accuracy and dependability of the diagnosis of cardiovascular disease.