Hyperspectral Imaging (HSI) has grown to be one of the most crucial optical imaging modalities with applications in numerous industries. The non‐invasive nature of HSI has led to widening its horizon to the medical domain, especially in areas like early diagnosis of various diseases. HSI combines both imaging and spectroscopy properties, thereby exploiting spectral and spatial dimensions of images captured, providing quick and accurate interpretation of data. The current study aims to give an exhaustive overview of HSI's applications pertaining to the medical industry for fast detection of diseases and aiding in surgical procedures. The survey focuses on hyperspectral imaging combined with various approaches ‐ machine learning, deep learning, genetic algorithms, and anomaly detection for the treatment of disorders. In addition, the survey highlights accompanying pre‐processing approaches, performance metrics, inferences, and future prospects of HSI in the medical domain. The current study can gauge computer vision specialists, researchers in machine and deep learning domain, doctors, and scientists by giving them a platform for improving existing treatment methods for the betterment of society.

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Object Recognition has become one of the most attractive areas of research for most of the scientists over the past few decades. Object recognition has extensive applications in numerous areas of interest. In this paper, the importance of object recognition in different applications has been highlighted. The very famous and impressive technique by David Lowe which is Scale Invariant Feature Transform (SIFT) has been implemented for object recognition and an attempt has been done to compare the results obtained from it with the another very important technique called Speeded-Up Robust Feature Transform (SURF) to conclude with certain interesting results.

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