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EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:

          This is a computer-aided diagnosis system for melanoma. The novelty lies in the optimized selection and integration of features derived from textural, border based, and geometrical properties of the melanoma lesion.

          Classification is done through the use of four classifiers; namely, support vector machine, random forest, logistic model tree, and hidden naive Bayes.

PROPOSED CONCEPT:

          This concept computer-aided method for the skin lesion classification applicable to both melanocytic skin lesions (MSLs) and nonmelanocytic skin lesions (NoMSLs). The computer-aided skin lesion classification has drawn attention as an aid for detection of skin cancers.

EXISTING  TECHNIQUE :

          WAVELET-DECOMPOSITION AND BOUNDARY-SERIES MODEL

PROPOSED ALGORITHM:

           TASK DECOMPOSITION STRATEGY  AND FLAT MODEL

TECHNIQUE DEFINITION:

          The visual characteristics of a lesion which constitutes the basis of clinical diagnostic approaches can be captured through texture analysis. According to the ABCD rule of dermoscopy, asymmetry is given the highest weight among the four features of asymmetry, border irregularity, color, and differential  structures.

ALGORITHM DEFINITION:

           From each skin lesion image, we extracted the border between the tumor and the surrounding normal skin area. Accurate border detection usually results in better classification performance. The general border detection algorithm is used for both MSLs and NoMSLs.

DRAWBACKS:

          The major disadvantage of this system is less flexible and less efficient and it cannot be operated by patients it need physicians or trained professional to operate.

          It has several components and it’s complicated to work.

          Images are in different conditions, the border detection step in two-third of them is done manually so less accuracy and efficiency.

          Slow and low resolution images.

ADVANTAGES:

          High efficiency and flexibility in exploiting the discriminant power of different types of features and therefore improving the recognition accuracy

          This technique confers the advantages to both physicians and patients by eliminating the need for clinic appointments, data privacy and physician training issues have been raised.

          Automated or semi-automated segmentation methods would have improvements in efficiency and accuracy.