Vol 22, No 3 (2023)

The brain is regarded as one of the most effective body-controlling organs. The development of technology has enabled the early and accurate detection of brain tumors, which makes a significant difference in their treatment. The adoption of AI has grown substantially in the arena of neurology. This systematic review compares recent Deep Learning (DL), Machine Learning (ML), and hybrid methods for detecting brain cancers. This article evaluates 36 recent articles on these techniques, considering datasets, methodology, tools used, merits, and limitations. The articles contain comprehensible graphs and tables. The detection of brain tumors relies heavily on ML techniques such as Support Vector Machines (SVM) and Fuzzy C-Means (FCM). Recurrent Convolutional Neural Networks (RCNN), DenseNet, Convolutional Neural Networks (CNN), ResNet, and Deep Neural Networks (DNN) are DL techniques used to detect brain tumors more efficiently. DL and ML techniques are merged to develop hybrid techniques. In addition, a summary of the various image processing steps is provided. The systematic review identifies outstanding issues and future goals for DL and ML-based techniques for detecting brain tumors. Through a systematic review, the most effective method for detecting brain tumors can be identified and utilized for improvement.

Many Digital Signal Processing (DSP) applications and electronic gadgets today require digital filtering. Different optimization algorithms have been used to obtain fast and improved results. Several researchers have used Enhanced Slime Mould Algorithm for designing the 2D IIR filter. However, it is observed that the Enhanced Slime Mould Algorithm did not achieve a better solution structure and had a slower convergence rate. In order to overcome the issue a fused ESMA-pelican Optimization Algorithm (FEPOA) is utilized for designing the 2D IIR filter which incorporates the pelican Optimization Algorithm with the Enhanced slime Mould Algorithm (ESMA). At first, the Chaotic Approach is utilized to initialize the population which provides the high-quality population with excellent population diversity, after that the position of population members is to identify and correct the individual in the boundary search region. After that, by the pelican Tactical Approach is to examine the search space and exploration power of the FEPOA, then the Fitness is calculated randomly, and the best solution will be upgraded and then moved towards the iterations. It repeats the FEPOA phases until the execution completes. Then the best solution gives the optimal solution, which enhances the speed of convergence, convergence accuracy and the performances of FEPOA. The FEPOA is then implemented in the IIR filter to improve the overall filter design. The results provided by FEPOA accomplish the necessary fitness and best solution for 200 iterations, and the amplitude response will achieve the maximum value for =2,4,8 as well as the execution time of 3.0158s, which is much quicker than the other Genetic Algorithms often used for 2D IIR filters.