Securing healthcare medical image information using advance morphological component analysis, information hiding...

Health care images contain a variety of imaging information that has specific features, which can make it challenging to assess and decide on the methods necessitated to safeguard the highly classified visuals from unauthorized exposure during transmission in a communication channel. As a result, this proposed approach utilizes a variety of techniques that will enhance the quality of textual healthcare images, communicate information securely, and interpret textual data from healthcare visuals without difficulty. Natural interference, primarily on the receiver side, reduces text-based healthcare image contrast, and numerous artifacts and adjacent picture element values impede diagnosis. Therefore, at the transmission end, the suggested method uses morphological component analysis to improve the contrast of textual healthcare images. Subsequently, it masks this textual healthcare image behind the cover image using steganography, maintaining the secrecy of private information during transmission on the Internet of Medical Things (IoMT) network. After obtaining the stego-image, reverse steganography is used to separate the textual health care image from the cover image. Following that, pre-processing had been performed utilizing weighted guided image filters to ensure that a text-based health care image would not be altered when data had been sent through an IoMT. After that, the Gabor Transform (GT) and stroke width transform are then used to extract the features required for a weighted classification approach that distinguishes between healthcare images with and without text content. Employing the cultural emperor penguin optimization strategy strengthened the performance of the weighted naive Bayes classifier. Later, a hybrid convolutional neural network with enhanced cuckoo search optimization is utilized to detect textual information in healthcare images. A variety of indicators are utilized to evaluate each cover picture and text-based healthcare image. These are accuracy, precision, recall, sensitivity, specificity, structural similarity index, peak signal-to-noise ratio, number of bytes of embedded and recovered input health-care textual pictures, and mean square error. The findings show that the proposed strategy outperforms all of the existing approaches. The suggested method successfully retrieves content at the receiver end. However, a few characters may be misplaced or recovered many times due to weighted guided image filtration halo artifacts, which impair image quality and provide inaccurate textual data.