A Modified Binary PSO Based Feature Selection for Automatic Lesion Detection in Mammograms

Abstract

This paper presents an effective feature selection method that can be applied to build a computer aided diagnosis system for breast cancer in order to discriminate between healthy, benign and malignant parenchyma. Determining the optimal feature set from a large set of original features is an important preprocessing step which removes irrelevant and redundant features and thus improves computational efficiency, classification accuracy and also simplifies the classifier structure. A modified binary particle swarm optimized feature selection method (MBPSO)has been proposed where k-Nearest Neighbour algorithm with leave-one-out cross validation serves as the fitness function. Digital mammograms obtained from Regional Cancer Centre, Thiruvananthapuram and the mammograms from web accessible mini-MIAS database has been used as the dataset for this experiment. Region of interests from the mammograms are automatically detected and segmented. A total of 117 shape, texture and histogram features are extracted from the ROIs. Significant features are selected using the proposed feature selection method.Classification is performed using feed forward artificial neural networks with back propagation learning. Receiver operating characteristics (ROC) and confusion matrix are used to evaluate the performance. Experimental results show that the modified binary PSO feature selection method not only obtains better classification accuracy but also simplifies the classification process as compared to full set of features. The performance of the modified BPSO is found to be at par with other widely used feature selection techniques.

Klasifikasi Cerita Bahasa Indonesia menggunakan Metode Hybrid PSO-KNN (Modified Binary Particle Swarm Optimization dengan K-Nearest Neighbor)

“…Hasil penelitian ini menunjukkan bahwa metode KNN memiliki nilai akurasi tertinggi jika dibandingkan dengan metode naïves bayes dan term-graph. Selanjutnya penelitian mengenai seleksi fitur dilakukan oleh Sheba K.U [5] dengan judul "A Modified Binary PSO based Feature Selection for Automatic Lesion Detection in Mammograms" yang mana penelitian ini menggunakan PSO yang telah dimodifikasi yakni Modified Binary Particle Swarm Optimization (MBPSO) untuk mencari subset fitur yang optimal dan dievaluasi menggunakan KNN. Dimana metode PSO-KNN yang diusulkan berhasil mengurangi kompleksitas komputasional dan mamp u menunjukkan efisiensi yang tinggi dbandingkan metode seleksi fitur lainnya dengan tingkat akurasi mencapai 97,2% dengan jumlah fitur optimal sebanyak 6 yang sebelumnya jumlah fitur asli sebanyak 117 dengan tingkat akurasi 87,3%.…”