Osteosarcoma is the malignant bone sarcoma that is characterized by widespread genomic disruption and the inclination for metastatic spread. Early detection of osteosarcoma increases the survival rate. Various osteosarcoma detection methods are adopted to detect osteosarcoma at an early stage, but evaluating the slides under the microscope to find the degree of tumor necrosis and tumor result is a major challenge in the medical sector. Hence, an effective detection method is developed using the proposed Fractional-Harris Hawks Optimization-based Generative Adversarial Network (F-HHO-based GAN) for detecting osteosarcoma at an early stage. Here, the proposed F-HHO is designed by the integration of Fractional Calculus and HHO, respectively. Accordingly, the classification of viable tumor, nontumor, and the necrotic tumor is carried out by GAN using the histology image slides. GAN is used to perform osteosarcoma detection based on the features extracted from the image through the process of cell segmentation. The training process of GAN is done using the proposed F-HHO algorithm. However, the proposed F-HHO obtained better performance using the metrics, namely, accuracy, sensitivity, and specificity with the values of 98%, 98%, and 98% for training percentage and 96.282%, 97.552%, and 95.651% for K-fold, respectively.

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The field of Image processing makes a high impact in the era of fast growing technology to increase or to satisfy the human comfort level. A single image may contain thousand times more information than a written text on piece of paper. But due to the advent of technology, number of image formats exists to provide strength to the image data like JPEG, Tiff, BMP, Gif etc. Due to this change in technology and the existence of these different formats, high resolution images are produced and require more memory for the purpose of storage. Even when we want to communicate on the basis of these images through Internet for some purpose then the issue arises and affect the communication. To deal with this issue some compression mechanism is required. In case of Image procession we can either have lossless image compression or lossy image compression. In this paper a lossless technique of Image processing is proposed by considering Haar wavelet and Vector transform techniques. 97% compression percentage is achieved with the help to proposed method and when the results are compared with other techniques low SNR values and high RMSE values are achieved for the proposed system which shows its accurate behaviour.

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