It is important to assess a fish if it is fresh or not fresh because a not fresh fish can harm the human's health. Milkfish (Chanos chanos) is the most familiar fish in a market today. In present generations many sensor technologies have been created or develop to answer the drawbacks of conventional methods for freshness classification of fish. Most of these methods are still in the stage of laboratory research and need further improvements and exploration researches for practical applications. There are several techniques used for conversion of image and one of them is the image processing. It is automatic, efficient, and non-destructive method for segmentation of tissues and monitoring the freshness of the fish. This study, generally aimed to classify milkfish (chanos chanos) freshness into fresh and not fresh based on Trained Cascade Model and Coiflet Wavelet Filter and Support Vector Machine.The eyes, gills, and body tissues of milkfish (chanos chanos) are segmented using clustering based method and its feature are strategically extracted in the wavelet transformation domain using Coiflet wavelet filter. Six scaling was used and wavelet function coefficients so improved in pixel averaging and differencing lead to a smoother wavelet and increased capability in several imageprocessing techniques. The study showed the accuracy results of 85.407% in Region of Interest Detection and 98% in Confusion Matrix for classification. The study is considered as significant to fish vendor, fish grower and fish consumers. Generally, study showed that the method used had improved results in freshness classifications.

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