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AIM:To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS:A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration. In order to increase data processing time, every single image converted into a Jpeg image which has a resolution of 320x320. Accuracy, precision and recall rates were calculated after training of the algorithm. RESULTS:Following training, CNN achieved acceptable performance ratios of 0.854 to 0.944 for accuracy, 0.812 to 0.980 for precision and 0.738 to 0.907 for recall. Also, CNN was able to detect HGG cases even though there is no apparent mass lesion in the given image. CONCLUSION: Our preliminary findings demonstrate; currently proposed CNN model achieves acceptable performance results for the automatic detection of HGGs on T2-weighted images.

ÖZETBu makalede, steganografi tabanlı yeni bir kişisel güvenlik yaklaşımı sunulmuş ve pratik olarak kullanımı için ise bir klasör kilitleme yazılımı (STKK) geliştirilmiştir. Sunulan yaklaşım kullanıcılara, bilgisayardaki tüm dosyaların güvenliği için esnek ve güvenilir çözümler sunmaktadır. Steganografik çözümlerin yanı sıra AES, DES ve 3DES gibi bilinen kriptografik yaklaşımların da geliştirilen yazılıma eklenmesi ile güvenlik arttırılmıştır. Deneysel sonuçlar, geliştirilen çözümün steganalize karşı dayanıklı olduğunu ve tüm dosya tiplerini desteklediği için kişisel güvenliğin artırılmasına katkı sağlayacağını göstermektedir.Anahtar Kelimeler: Steganografi, steganaliz, güvenlik, klasör kilitleme yazılımı, kriptografi, DES, 3DES, AES DEVELOPMENT OF A NEW FOLDER LOCK APPRAOCH AND SOFTWARE BASED ON STEGANOGRAPHY ABSTRACTThis paper introduces a new personal security approach based on steganography. In order to achieve the task, a folder-lock software (STKK) was developed. The proposed approach provides more flexible solutions to the users to secure all files in the computer. In addition to steganographic solutions, cryptographic approaches such as AES, DES and 3DES are also added to the developed software. The experimental results have shown that the developed solution is robust against steganalysis and might provide better personel security by supporting all file types.

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