Brain tumors cause death to a lot of people globally. Brain tumor disease is seen as one of the most lethal diseases since its mortality rate is high. Nevertheless, this rate can be diminished if the disease is identified and treated early. Recently, healthcare providers have relied on computed tomography (CT) scans and magnetic resonance imaging (MRI) in their diagnosis. Currently, various artificial intelligence (AI)‐based solutions have been implemented to diagnose this disease early to prepare suitable treatment plans. In this article, we propose a novel self‐attention transfer adaptive learning approach (SATALA) to identify brain tumors. This approach is an automated AI‐based model that contains two deep‐learning technologies to determine the existence of brain tumors. In addition, the proposed approach categorizes the identified tumors into two groups, which are benign and malignant. The developed method incorporates two deep‐learning technologies: a convolutional neural network (CNN), which is VGG‐19, and a new UNET network architecture. This approach is trained and evaluated on six public datasets and attained exquisite results. It achieved an average of 95% accuracy and anF1‐score of 96.61%. The proposed approach was compared with other state‐of‐the‐art models that were reported in the related work. The conducted experiments show that the proposed approach generates exquisite outputs and exceeds other works in some scenarios. In conclusion, we can infer that the proposed approach provides trustworthy identifications of brain cancer and can be applied in healthcare facilities.

Currently, amyotrophic lateral sclerosis (ALS) disease is considered fatal since it affects the central nervous system with no cure or clear treatments. This disease affects the spinal cord, more specifically, the lower motor neurons (LMNs) and the upper motor neurons (UMNs) inside the brain along with their networks. Various solutions have been developed to predict ALS. Some of these solutions were implemented using different deep‐learning methods (DLMs). Nevertheless, this disease is considered a tough task and a huge challenge. This article proposes a reliable model to predict ALS disease based on a deep‐learning tool (DLT). The developed DLT is designed using a UNET architecture. The proposed approach is evaluated for different performance quantities on a dataset and provides promising results. An average obtained accuracy ranged between 82% and 87% with around 86% of the F‐score. The obtained outcomes can open the door to applying DLMs to predict and identify ALS disease.