Abstract: 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 into Inception ResNet. Finally, a dense neural network is employed to categorize the many forms of lung cancer, including lung nodules, small-cell lung cancer, and normal lung cancer. The proposed classification accuracy is 98.17%, which is extremely accurate. The proposed LUNCRDEN model improves overall accuracy by 4.17%, 7.32%, and 0.97% in comparison to support vector machines, GoogleNet, and convolutional neural networks, respectively.

Abstract: 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.

Abstract: 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.

Abstract: Leukemia is a critical condition affecting the blood and lymphatic systems, requiring timely and accurate detection for effective treatment. Traditional microscopic analysis, though effective, relies heavily on the skill of the pathologist, leading to potential delays in diagnosis. In this research, a novel introduced LeukNet proposed for identifying the different types of leukaemia in its early stages. Initially, the images of the blood smear are preprocessed using Adaptive Gaussian filters to removing noise artifacts. Sobel detector is used to detecting the edges in horizontal and Vertical from the images. Based on the edges, the deep learning-based GoogleNet is used for extracting the blood cell features. Further, the classification is performed by Multi-Layer Perceptron. It classifies the smear of blood images into five different classes: Normal, Acute leukemia (ALL), Acute lymphoblastic leukemia (ALL), Chronic lymphoblastic leukemia (CLL), and Chronic myeloid leukemia (CML). The effectiveness of the proposed LeukNet method using metrics like F1 score, sensitivity, accuracy, and specificity. The proposed LeukNet model achieved a classification accuracy 98.76%. The proposed LeukNet model enhanced the total accuracy by 2.90%, 6.57%, and 8.48% better than LD-C NMC, ALL-delt and DeepLeukNet respectively.

Abstract: Liver cancer (LC) occur when normal cells develop aberrant DNA alterations and reproduce uncontrollably. Patients with cirrhosis, hepatitis B or C, or both have an increased risk of developing the progressing stage of cancer. The radiologists spend more time for detecting the LC when analysing with traditional methods. Early detection of LC can help doctors and radiation therapists identify the tumours. However, manual identification of LC is time-intensive and challenging process in the current scenario. In this work, an automated deep learning-based LC-DCNN model is designed to classify the LC in its initial phase. At first, the CT scans are gathered from the publicly available LiTS database and these gathered images are pre-processed using Gaussian filter is used for reducing the noises and to smoothen the edges. The liver region is segmented using Enhanced otsu (EM) method is utilized to segment the liver region separately from the pre-processed input images. Afterwards, Dilated Convolutional Neural Network (DCNN) with the attention block is employed for classifying the LC into tri-classes such as normal controls (NC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) cases based on the extracted features. The effectiveness of the proposed LC-DCNN is evaluated using the attributes viz., accuracy, sensitivity, precision, specificity, and F1-score values are computed as classification results. The experimental fallouts disclose that the DA-CNN attains an accuracy range of 98.20%. Moreover, the proposed DA-CNN advances the overall accuracy by 3.25%, 5.29%, and 0.99% better than Optimised GAN, OPBS-SSHC, HFCNN respectively.