Evolution in diagnosis and detection of brain tumor – review

Abstract

Diagnosis of Brain tumor at an early stage has became an important topic of research in recent time. Detection of tumor at an early stage for primary treatment increases the patient’s survival rate. Processing of Magnetic resonance image (MRI) for an early tumor detection face the challenge of high processing overhead due to large volume of image input to the processing system. This result to large delay and decrease in system efficiency. Hence, the need of an enhanced detection system for accurate segmentation and representation for a faster and accurate processing has evolved in recent past. Development of new approaches based on improved learning and processing for brain tumor detection has been proposed in recent literatures. This paper outlines a brief review on the developments made in the area of MRI processing for an early diagnosis and detection of brain tumor for segmentation, representation and applying new machine learning (ML) methods in decision making. The learning ability and fine processing of Machine learning algorithms has shown an improvement in the current automation systems for faster and more accurate processing for brain tumor detection. The current trends in the automation of brain tumor detection, advantages, limitations and the future perspective of existing methods for computer aided diagnosis in brain tumor detection is outlined.

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

“…Thus, these methods need a rich dataset of training images. DL training is time-consuming with a complicated framework and necessitates a high level of computer system configuration 79 , 84 , 85 …”

“…DL training is time-consuming with a complicated framework and necessitates a high level of computer system configuration. 79,84,85 Thus, sparse-based medical image fusion techniques 19 have a better performance with lowcost resource requirements and a better running time. According to their nature, they also require low memory consumption for storage.…”

Revising Fourier-transform infrared (FT-IR) and Raman spectroscopy towards brain cancer detection

“…Brain cancers are traditionally detected by using a combination of techniques, such as imaging tests by means of magnetic resonance imaging (MRI), computed tomography (CT) or positron emission tomography (PET) scans to detect abnormal areas [25], together with tissue biopsies to identify the tumour type and grade by means of histopathology techniques [26]. These techniques show some disadvantages in comparison with FT-IR and Raman spectroscopy.…”

Scaffold‐based spheroid models of glioblastoma multiforme and its use in drug screening

“…As neural tissues possess distinctive cell and microenvironmental characteristics, these tumors can endure practically all traditional and cutting‐edge treatments. Brain cancers are divided into primary and metastatic brain tumors depending on where they originate (Sravanthi Peddinti et al, 2021). Gliomas are the most typical primary brain tumor type accounting for more than 70% of malignant tumors emanating from the central nervous system (Schwartzbaum et al, 2006).…”