Search

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a SupportVector Machine (SVM). DiscreteWavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial. [ABSTRACT FROM AUTHOR]

Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models. [ABSTRACT FROM AUTHOR]