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    Brain tumor classification using the modified ResNet50 model based on transfer learning
    (Elsevier Sci LTD, 2023) Sharma, Arpit Kumar; Nandal, Amita; Dhaka, Arvind; Zhou, Liang; Alenezi, Fayadh; Polat, Kemal
    Brain tumour classification is essential for determining the type and grade and deciding on therapy appropriately. Several diagnostic methods are used in the therapeutic therapy to identify brain tumours. MRI, on the other hand, offers superior picture clarity, which is why specialists depend on it. Furthermore, detecting cancer through the manual division of brain tumours is a time-consuming, exhausting, and difficult job. The handdesigned outlines for planned brain tumour growth methods are present in the majority of the instances. Segmentation is a highly reliable and precise method for assessing therapy prognosis, planning, and outcomes. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) advancements have enabled us to investigate the illness with high precision in a short period of time. Such technologies have produced some remarkable results, particularly in the last twenty years. Such breakthroughs provide doctors with the ability to evaluate the human anatomy using high-resolution sections. The most recent approaches can improve diagnostic precision when examining patients using non-invasive means. This work introduces a brain tumour detection method. The model grows using ResNet50, feature extraction, and augmentation. CNN's pre-trained datasets are used to fine-tune transfer learning. The proposed design utilised elements of the ResNet50 model, removing the final layer and adding four additional layers to meet work conditions. This study uses the improved ResNet50 model to present a novel deep-learning approach based on a transfer learning technique for evaluating brain cancer categorisation accuracy. Performance metrics were used to evaluate the effectiveness of the proposed model, and the results were compared to those obtained using state-of-the-art methods.
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    Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement
    (Springer, 2023) Zhou, Liang; Alenezi, Fayadh S.; Nandal, Amita; Dhaka, Arvind; Wu, Tao; Polat, Kemal
    The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overexposed and underexposed images into horizontal and vertical sub-bands using Discrete Wavelet Transform (DWT). The horizontal and vertical sub-bands are then enhanced using Caputo Differential Operator (CDO) and fused by taking the average of the transformed horizontal and vertical fractional derivatives. This work introduces a fractional derivative-based edge and feature enhancement to be used in conjuction with DWT and inverse DWT (IDWT) operations. The proposed algorithm combines the salient features of overexposed and underexposed images and enhances the fused image effectively. We use the fractional derivative-based method because it restores the edge and texture information more efficiently than existing method. In addition, we have introduced a resolution enhancement operator to correct and balance the overexposed and underexposed images, together with the Caputo enhanced fused image we obtain an image with significantly deepened resolution. Finally, we introduce a novel texture enhancing and smoothing operation to yield the final image. We apply subjective and objective evaluations of the proposed algorithm in direct comparison with other existing image fusion methods. Our approach results in aesthetically subjective image enhancement, and objectively measured improvement metrics.
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    Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement (nov, 10.1007/s10489-022-04344-z, 2022)
    (Springer, 2023) Zhou, Liang; Alenezi, Fayadh S. S.; Nandal, Amita; Dhaka, Arvind; Wu, Tao; Polat, Kemal
    The article Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement, written by Liang Zhou, Fayadh S. Alenezi, Amita Nandal, Arvind Dhaka, Tao Wu, Deepika Koundal, Adi Alhudhaif and Kemal Polat, was originally published electronically on the publisher’s internet portal on November 29, 2022 without open access.