Skin malignant growth is quite possibly the most commonly seen Malignancy type in people. Skin disease happens because of the un controllable developing of transformations occurring in DNAs developing to certain reasons. Perceiving the malignant growth in beginning phases could build the opportunity of an effective treatment. These days, PC helped finding applications are utilized nearly at each field. From the real dermo scopic images, the first-stage network aims for precise segmentation of the skin lesion. The second-stage network is a classification network that can predict the existence of Melanoma and Squamous Cell Carcinoma in a skin sample. Deep convolutional neural networks, such as Inception-v4, ResNet-152, and DenseNet-161, were trained for melanoma and squamous cell carcinoma detection and seborrheickeratosis classification. U-Net with VGG-16 Encoder was trained to create segmentation masks for lesion segmentation. Resnet engineering achieves the highest precision of 90 percent among the equations used in the proposed models.

The conventional techniques and algorithms employed by forensic scientists to assist in the identification of individuals on the basis of their respective Deoxyribonucleic acid base(DNA) pair profiles involves more computational steps and mathematical formulas that leads to more complexity. DNA identification is not considered by many as a biometric recognition technology, mainly because it is not yet an automated process i.e. it takes more time to analyze the DNA finger prints and samples collected from the crime scene, it will be considered as a future biometric trait if it's suitably automated. Neural networks learn by examples so that it can be trained with known examples of a problem to gain knowledge about it so the neural network can be effective to solve unknown or untrained instances of the problem if it is aptly trained. The perfect blend made of bioinformatics, neural networks and fuzzy logic results in efficient algorithms of pattern analysis techniques that induce automation which is inevitable in DNA profiling that became manually impractical with the growing amount of data.