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In this paper conventional Playfair Cipher is being modified by encrypting the plaintext in blocks. For each block the keyword would be the same but the matrix will shift by some random value. As a result of which the diagram analysis would be very difficult which is done in the traditional Playfair Cipher to obtain the plaintext from the ciphertext. The shift value will be generated using SHA-1 which is very secure. Playfair Cipher method, based on polyalphabetic cipher is relatively easy to break because it still leaves much of the structure and a few hundred of letters of ciphertext are sufficient. To add to its security and to make it more usable we are using 6x6 matrix instead of 5x5 which will be able to cover 26 alphabets in English and ten numerals i.e. from 0 to 9. This 6x6 matrix eliminate the case of putting of 2 alphabets (I and J) together in the matrix as it was in the 5x5 matrix. Plaintext as well as key can be numeral, alphabetic or combination of both.

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Leukemia is type of cancer in blood which impacts the lymphatic framework and the bone marrow and also impacts white blood cells. Leukemia, in contrast to other types of cancer, does not produce solid tumors; instead, it produces a huge number of aberrant white blood cells that crowd out the healthy blood cells. Machine learning algorithms that are widely utilized in the treatment of leukemia, whether it is to classify the various forms of leukemia or to determine whether a patient has the disease. It is a malignant kind of cancer that results in a number of medical issues. Expert hematologists and pathologists manually examine blood samples under the microscope to make a diagnosis. Techniques like image processing and pattern recognition can be utilized to help these experts. In order to attain excellent performance in the categorization of malignant leukocytes challenge, this paper suggests straightforward modifications to conventional neural network topologies. Consequently, there is considerable interest in the trustworthy and precise recognition of nonmalignant and malignant cells. Leukemia can be automatically detected using computer-aided diagnostic (CAD) models, which can help doctors and be useful for leukemia early identification. In this single-center study, we attempted to develop a deep learning model for classification of leukemic B-lymphoblasts. Data augmentation methods were utilized to manage the little dataset size and an exchange learning technique was utilized to accelerate learning and upgrade the presentation of the recommended network in order to create a trustworthy and accurate deep learner. The outcomes demonstrate that our suggested approach surpassed seperate networks with a test accuracy of 95.59% in the Leukemic B-lymphoblast examination, and was caable to merge characteristics extracted from the top deep learning models.

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