## 4 Conclusions  
In this article, we propose an Ultra-Light DL framework for features extraction forming HFS with textural features for brain tumors' detection and resections T1-weighted CE-MRI images, based on 233 patients across the transverse, coronal, and sagittal planes with 3064 instances of three types of brain tumors: (glioma, meningioma, and pituitary). In the case of brain tumors, early detection and its automated classification is of prime importance and is still considered an open challenge to date. The tumor position, its relationship with its contiguous cells, its texture, and numerous perimeters affecting the MRI scans are some of the major highlights complicating its detection. Radiologists' manual scanning procedure, although tedious, especially when the number of scans is enormous, is the only path to success. The salient features of this work include that it has achieved the outclass prediction accuracy with minimum false negatives for modern PC category average GPU-usage facility, with only 20 epochs to extract dynamic features using UL-DLA with a minimum number of diligently tuned layers and exploiting textural features to identify pair-wise contiguous pixel relationships causing improved discrimination identified through SVM classifier. The proposed methodology does not require any preprocessing or segmentation of the tumor region. The details related to assessing the complexity and reliability performance of the proposed system have also been carried out. To the best of our knowledge in the literature, the test results using the proposed method have the highest detection rate of 99.24% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) with 7 FNs for glioma, meningioma, and pituitary tumors, respectively. Our results have been found to be 2% better than the previously best known 97.30% in the PC desktop category system, indicating that our proposed system is highly capable of increasing diagnostic assistance to brain tumor radiologists. We strongly recommend the proposed system to act as a second opinion to the radiologists and clinical experts for this highly effective decision support system for the early diagnosis of the vulnerable population against brain tumors. The UL-BTD system has a low computational cost, with 11.69 ms detection-time per image, using even a modern PC system having average GPU resources. The proposed method has the potential use in brain tumor real-time surgery with a reduced amount of time (22.07% less) in comparison to the state-of-the-art earlier to detect a tumor without any dedicated hardware providing a route for a desktop application in brain surgery.  
Conceptualized the study, analyzed data, and wrote the paper, revised and edited, S.A.Q.; conceptualized the study, analyzed data, and wrote the paper, revised and edited, S.E.A.R.; analyzed data, revised, edited and supervised, L.H.; edited the paper, M.K.N.; edited the paper, A.A.M.; analyzed the data, A.u.R.; analyzed the data and edited the paper, F.N.A.-W.; edited the paper, A.M.H. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP 2/18/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R151), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4310373 DSR07).  
Not applicable as the dataset is publically available.  
Previously reported data were used to support this study and are available at ([[https://github.com/chengjun583/brainTumorRetrieval](https://github.com/chengjun583/brainTumorRetrieval)]([https://github.com/chengjun583/brainTumorRetrieval](https://github.com/chengjun583/brainTumorRetrieval)), accessed on 1 November 2021).  
The authors declare no conflict of interest.